Overview

Dataset statistics

 RealSynthetic
Number of variables4242
Number of observations494021494021
Missing cells00
Missing cells (%)0.0%0.0%
Duplicate rows00
Duplicate rows (%)0.0%0.0%
Total size in memory158.3 MiB158.3 MiB
Average record size in memory336.0 B336.0 B

Variable types

 RealSynthetic
Numeric1414
Categorical2727
Text11

Alerts

RealSynthetic
is_host_login has constant value "" is_host_login has constant value "" Constant
src_bytes is highly overall correlated with count and 2 other fieldssrc_bytes is highly overall correlated with dst_host_srv_countHigh Correlation
dst_bytes is highly overall correlated with count and 1 other fieldsdst_bytes is highly overall correlated with count and 1 other fieldsHigh Correlation
hot is highly overall correlated with num_compromised and 1 other fieldshot is highly overall correlated with num_compromised and 1 other fieldsHigh Correlation
num_compromised is highly overall correlated with hot and 1 other fieldsnum_compromised is highly overall correlated with hot and 1 other fieldsHigh Correlation
num_root is highly overall correlated with su_attemptednum_root is highly overall correlated with su_attemptedHigh Correlation
num_access_files is highly overall correlated with su_attemptednum_access_files is highly overall correlated with su_attemptedHigh Correlation
count is highly overall correlated with src_bytes and 13 other fieldscount is highly overall correlated with dst_bytes and 11 other fieldsHigh Correlation
srv_count is highly overall correlated with src_bytes and 10 other fieldssrv_count is highly overall correlated with count and 5 other fieldsHigh Correlation
dst_host_count is highly overall correlated with dst_bytes and 2 other fieldsdst_host_count is highly overall correlated with dst_bytes and 2 other fieldsHigh Correlation
dst_host_srv_count is highly overall correlated with src_bytes and 10 other fieldsdst_host_srv_count is highly overall correlated with src_bytes and 9 other fieldsHigh Correlation
protocol_type is highly overall correlated with count and 10 other fieldsprotocol_type is highly overall correlated with count and 10 other fieldsHigh Correlation
flag is highly overall correlated with serror_rate and 10 other fieldsflag is highly overall correlated with protocol_type and 11 other fieldsHigh Correlation
logged_in is highly overall correlated with count and 3 other fieldslogged_in is highly overall correlated with count and 2 other fieldsHigh Correlation
su_attempted is highly overall correlated with num_compromised and 2 other fieldssu_attempted is highly overall correlated with num_compromised and 2 other fieldsHigh Correlation
is_guest_login is highly overall correlated with hotis_guest_login is highly overall correlated with hotHigh Correlation
serror_rate is highly overall correlated with count and 10 other fieldsserror_rate is highly overall correlated with count and 9 other fieldsHigh Correlation
srv_serror_rate is highly overall correlated with count and 10 other fieldssrv_serror_rate is highly overall correlated with count and 9 other fieldsHigh Correlation
rerror_rate is highly overall correlated with flag and 3 other fieldsrerror_rate is highly overall correlated with flag and 3 other fieldsHigh Correlation
srv_rerror_rate is highly overall correlated with flag and 3 other fieldssrv_rerror_rate is highly overall correlated with flag and 3 other fieldsHigh Correlation
same_srv_rate is highly overall correlated with count and 10 other fieldssame_srv_rate is highly overall correlated with count and 10 other fieldsHigh Correlation
diff_srv_rate is highly overall correlated with dst_host_diff_srv_ratediff_srv_rate is highly overall correlated with dst_host_diff_srv_rateHigh Correlation
dst_host_same_srv_rate is highly overall correlated with count and 10 other fieldsdst_host_same_srv_rate is highly overall correlated with count and 10 other fieldsHigh Correlation
dst_host_diff_srv_rate is highly overall correlated with diff_srv_rate and 1 other fieldsdst_host_diff_srv_rate is highly overall correlated with diff_srv_rate and 1 other fieldsHigh Correlation
dst_host_same_src_port_rate is highly overall correlated with count and 11 other fieldsdst_host_same_src_port_rate is highly overall correlated with count and 10 other fieldsHigh Correlation
dst_host_serror_rate is highly overall correlated with count and 10 other fieldsdst_host_serror_rate is highly overall correlated with count and 9 other fieldsHigh Correlation
dst_host_srv_serror_rate is highly overall correlated with count and 10 other fieldsdst_host_srv_serror_rate is highly overall correlated with count and 9 other fieldsHigh Correlation
dst_host_rerror_rate is highly overall correlated with flag and 3 other fieldsdst_host_rerror_rate is highly overall correlated with flag and 3 other fieldsHigh Correlation
dst_host_srv_rerror_rate is highly overall correlated with flag and 3 other fieldsdst_host_srv_rerror_rate is highly overall correlated with flag and 3 other fieldsHigh Correlation
label is highly overall correlated with logged_in and 2 other fieldslabel is highly overall correlated with logged_in and 1 other fieldsHigh Correlation
flag is highly imbalanced (71.1%) flag is highly imbalanced (50.4%) Imbalance
land is highly imbalanced (99.9%) land is highly imbalanced (99.9%) Imbalance
wrong_fragment is highly imbalanced (98.3%) wrong_fragment is highly imbalanced (97.5%) Imbalance
urgent is highly imbalanced (> 99.9%) urgent is highly imbalanced (> 99.9%) Imbalance
root_shell is highly imbalanced (99.8%) root_shell is highly imbalanced (99.9%) Imbalance
su_attempted is highly imbalanced (> 99.9%) su_attempted is highly imbalanced (> 99.9%) Imbalance
num_shells is highly imbalanced (99.9%) num_shells is highly imbalanced (99.8%) Imbalance
is_guest_login is highly imbalanced (98.5%) is_guest_login is highly imbalanced (97.9%) Imbalance
rerror_rate is highly imbalanced (69.4%) rerror_rate is highly imbalanced (55.0%) Imbalance
srv_rerror_rate is highly imbalanced (68.5%) srv_rerror_rate is highly imbalanced (54.2%) Imbalance
diff_srv_rate is highly imbalanced (95.6%) diff_srv_rate is highly imbalanced (95.1%) Imbalance
srv_diff_host_rate is highly imbalanced (87.9%) srv_diff_host_rate is highly imbalanced (82.2%) Imbalance
dst_host_diff_srv_rate is highly imbalanced (96.2%) dst_host_diff_srv_rate is highly imbalanced (96.0%) Imbalance
dst_host_srv_diff_host_rate is highly imbalanced (99.1%) dst_host_srv_diff_host_rate is highly imbalanced (98.8%) Imbalance
dst_host_rerror_rate is highly imbalanced (70.2%) dst_host_rerror_rate is highly imbalanced (55.9%) Imbalance
dst_host_srv_rerror_rate is highly imbalanced (70.5%) dst_host_srv_rerror_rate is highly imbalanced (56.5%) Imbalance
label is highly imbalanced (65.3%) label is highly imbalanced (60.3%) Imbalance
duration is highly skewed (γ1 = 25.86485736) Alert not present in this datasetSkewed
src_bytes is highly skewed (γ1 = 699.2131508) src_bytes is highly skewed (γ1 = 240.2597569) Skewed
dst_bytes is highly skewed (γ1 = 136.7592782) dst_bytes is highly skewed (γ1 = 94.51300204) Skewed
hot is highly skewed (γ1 = 32.62914514) hot is highly skewed (γ1 = 27.07183851) Skewed
num_failed_logins is highly skewed (γ1 = 160.8026164) num_failed_logins is highly skewed (γ1 = 78.09030246) Skewed
num_compromised is highly skewed (γ1 = 417.5302281) num_compromised is highly skewed (γ1 = 209.4544452) Skewed
num_root is highly skewed (γ1 = 417.0658359) num_root is highly skewed (γ1 = 260.6141868) Skewed
num_file_creations is highly skewed (γ1 = 192.3347657) num_file_creations is highly skewed (γ1 = 180.1583285) Skewed
num_access_files is highly skewed (γ1 = 61.20145172) num_access_files is highly skewed (γ1 = 50.78371256) Skewed
Unnamed: 0 is uniformly distributed Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unnamed: 0 has unique values Unique
duration has 481671 (97.5%) zeros duration has 454559 (92.0%) zeros Zeros
src_bytes has 115342 (23.3%) zeros src_bytes has 193325 (39.1%) zeros Zeros
dst_bytes has 408258 (82.6%) zeros dst_bytes has 372649 (75.4%) zeros Zeros
hot has 490829 (99.4%) zeros hot has 489530 (99.1%) zeros Zeros
num_failed_logins has 493958 (> 99.9%) zeros num_failed_logins has 493926 (> 99.9%) zeros Zeros
num_compromised has 491797 (99.5%) zeros num_compromised has 491868 (99.6%) zeros Zeros
num_root has 493436 (99.9%) zeros num_root has 492697 (99.7%) zeros Zeros
num_file_creations has 493756 (99.9%) zeros num_file_creations has 493808 (> 99.9%) zeros Zeros
num_access_files has 493567 (99.9%) zeros num_access_files has 493323 (99.9%) zeros Zeros
Alert not present in this datasetcount has 9930 (2.0%) zeros Zeros
Alert not present in this datasetsrv_count has 11957 (2.4%) zeros Zeros

Reproduction

 RealSynthetic
Analysis started2023-12-25 15:59:00.3262582023-12-25 16:01:19.873693
Analysis finished2023-12-25 16:01:19.8575822023-12-25 16:03:32.388279
Duration2 minutes and 19.53 seconds2 minutes and 12.51 seconds
Software versionydata-profiling vv4.5.1ydata-profiling vv4.5.1
Download configurationconfig.jsonconfig.json

Variables

Unnamed: 0
Real number (ℝ)

 RealSynthetic
Distinct494021494021
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean247010247010
 RealSynthetic
Minimum00
Maximum494020494020
Zeros11
Zeros (%)< 0.1%< 0.1%
Negative00
Negative (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:33.358663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

 RealSynthetic
Minimum00
5-th percentile2470124701
Q1123505123505
median247010247010
Q3370515370515
95-th percentile469319469319
Maximum494020494020
Range494020494020
Interquartile range (IQR)247010247010

Descriptive statistics

 RealSynthetic
Standard deviation142611.72142611.72
Coefficient of variation (CV)0.577352020.57735202
Kurtosis-1.2-1.2
Mean247010247010
Median Absolute Deviation (MAD)123505123505
Skewness-3.430305 × 10-16-3.430305 × 10-16
Sum1.2202813 × 10111.2202813 × 1011
Variance2.0338104 × 10102.0338104 × 1010
MonotonicityStrictly increasingStrictly increasing
2023-12-26T00:03:33.749153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
329388 1
 
< 0.1%
329356 1
 
< 0.1%
329355 1
 
< 0.1%
329354 1
 
< 0.1%
329353 1
 
< 0.1%
329352 1
 
< 0.1%
329351 1
 
< 0.1%
329350 1
 
< 0.1%
329349 1
 
< 0.1%
Other values (494011) 494011
> 99.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
329388 1
 
< 0.1%
329356 1
 
< 0.1%
329355 1
 
< 0.1%
329354 1
 
< 0.1%
329353 1
 
< 0.1%
329352 1
 
< 0.1%
329351 1
 
< 0.1%
329350 1
 
< 0.1%
329349 1
 
< 0.1%
Other values (494011) 494011
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%

duration
Real number (ℝ)

 RealSynthetic
Distinct24955653
Distinct (%)0.5%1.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean47.97930279.347479
 RealSynthetic
Minimum00
Maximum5832948570
Zeros481671454559
Zeros (%)97.5%92.0%
Negative00
Negative (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:34.131567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

 RealSynthetic
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile04
Maximum5832948570
Range5832948570
Interquartile range (IQR)00

Descriptive statistics

 RealSynthetic
Standard deviation707.74647872.83749
Coefficient of variation (CV)14.75107911.000192
Kurtosis942.53024489.53616
Mean47.97930279.347479
Median Absolute Deviation (MAD)00
Skewness25.86485718.42465
Sum2370278339199321
Variance500905.07761845.28
MonotonicityNot monotonicNot monotonic
2023-12-26T00:03:34.488914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 481671
97.5%
1 2476
 
0.5%
2 870
 
0.2%
3 625
 
0.1%
5 554
 
0.1%
2630 496
 
0.1%
4 413
 
0.1%
14 322
 
0.1%
10 194
 
< 0.1%
7 169
 
< 0.1%
Other values (2485) 6231
 
1.3%
ValueCountFrequency (%)
0 454559
92.0%
1 6331
 
1.3%
2 3755
 
0.8%
3 2732
 
0.6%
4 2221
 
0.4%
5 1844
 
0.4%
6 1483
 
0.3%
7 1196
 
0.2%
8 1082
 
0.2%
9 920
 
0.2%
Other values (5643) 17898
 
3.6%
ValueCountFrequency (%)
0 481671
97.5%
1 2476
 
0.5%
2 870
 
0.2%
3 625
 
0.1%
4 413
 
0.1%
5 554
 
0.1%
6 157
 
< 0.1%
7 169
 
< 0.1%
8 103
 
< 0.1%
9 121
 
< 0.1%
ValueCountFrequency (%)
0 454559
92.0%
1 6331
 
1.3%
2 3755
 
0.8%
3 2732
 
0.6%
4 2221
 
0.4%
5 1844
 
0.4%
6 1483
 
0.3%
7 1196
 
0.2%
8 1082
 
0.2%
9 920
 
0.2%
ValueCountFrequency (%)
0 454559
92.0%
1 6331
 
1.3%
2 3755
 
0.8%
3 2732
 
0.6%
4 2221
 
0.4%
5 1844
 
0.4%
6 1483
 
0.3%
7 1196
 
0.2%
8 1082
 
0.2%
9 920
 
0.2%
ValueCountFrequency (%)
0 481671
97.5%
1 2476
 
0.5%
2 870
 
0.2%
3 625
 
0.1%
4 413
 
0.1%
5 554
 
0.1%
6 157
 
< 0.1%
7 169
 
< 0.1%
8 103
 
< 0.1%
9 121
 
< 0.1%

protocol_type
Categorical

 RealSynthetic
Distinct33
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
icmp
283602 
tcp
190065 
udp
 
20354
tcp
291120 
icmp
176720 
udp
 
26181

Length

 RealSynthetic
Max length44
Median length43
Mean length3.57406873.3577176
Min length33

Characters and Unicode

 RealSynthetic
Total characters17656651658783
Distinct characters77
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st rowtcptcp
2nd rowtcpudp
3rd rowicmpicmp
4th rowicmpicmp
5th rowtcpicmp

Common Values

ValueCountFrequency (%)
icmp 283602
57.4%
tcp 190065
38.5%
udp 20354
 
4.1%
ValueCountFrequency (%)
tcp 291120
58.9%
icmp 176720
35.8%
udp 26181
 
5.3%

Length

2023-12-26T00:03:34.815831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:35.081518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:35.266569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
icmp 283602
57.4%
tcp 190065
38.5%
udp 20354
 
4.1%
ValueCountFrequency (%)
tcp 291120
58.9%
icmp 176720
35.8%
udp 26181
 
5.3%

Most occurring characters

ValueCountFrequency (%)
p 494021
28.0%
c 473667
26.8%
i 283602
16.1%
m 283602
16.1%
t 190065
 
10.8%
u 20354
 
1.2%
d 20354
 
1.2%
ValueCountFrequency (%)
p 494021
29.8%
c 467840
28.2%
t 291120
17.6%
i 176720
 
10.7%
m 176720
 
10.7%
u 26181
 
1.6%
d 26181
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1765665
100.0%
ValueCountFrequency (%)
Lowercase Letter 1658783
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 494021
28.0%
c 473667
26.8%
i 283602
16.1%
m 283602
16.1%
t 190065
 
10.8%
u 20354
 
1.2%
d 20354
 
1.2%
ValueCountFrequency (%)
p 494021
29.8%
c 467840
28.2%
t 291120
17.6%
i 176720
 
10.7%
m 176720
 
10.7%
u 26181
 
1.6%
d 26181
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 1765665
100.0%
ValueCountFrequency (%)
Latin 1658783
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 494021
28.0%
c 473667
26.8%
i 283602
16.1%
m 283602
16.1%
t 190065
 
10.8%
u 20354
 
1.2%
d 20354
 
1.2%
ValueCountFrequency (%)
p 494021
29.8%
c 467840
28.2%
t 291120
17.6%
i 176720
 
10.7%
m 176720
 
10.7%
u 26181
 
1.6%
d 26181
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1765665
100.0%
ValueCountFrequency (%)
ASCII 1658783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 494021
28.0%
c 473667
26.8%
i 283602
16.1%
m 283602
16.1%
t 190065
 
10.8%
u 20354
 
1.2%
d 20354
 
1.2%
ValueCountFrequency (%)
p 494021
29.8%
c 467840
28.2%
t 291120
17.6%
i 176720
 
10.7%
m 176720
 
10.7%
u 26181
 
1.6%
d 26181
 
1.6%

service
['Text', 'Text']

 RealSynthetic
Distinct6665
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:36.110157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

 RealSynthetic
Max length1111
Median length510
Mean length5.36997215.4601869
Min length33

Characters and Unicode

 RealSynthetic
Total characters26528792697447
Distinct characters3838
Distinct categories44 ?
Distinct scripts22 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique30 ?
Unique (%)< 0.1%0.0%

Sample

 RealSynthetic
1st rowsmtpname
2nd rowhttpprivate
3rd rowecr_iecr_i
4th rowecr_iecr_i
5th rowprivateecr_i
ValueCountFrequency (%)
ecr_i 281400
57.0%
private 110893
 
22.4%
http 64293
 
13.0%
smtp 9723
 
2.0%
other 7237
 
1.5%
domain_u 5863
 
1.2%
ftp_data 4721
 
1.0%
eco_i 1642
 
0.3%
ftp 798
 
0.2%
finger 670
 
0.1%
Other values (56) 6781
 
1.4%
ValueCountFrequency (%)
ecr_i 164230
33.2%
http 63574
 
12.9%
private 37740
 
7.6%
printer 27701
 
5.6%
pop_3 18844
 
3.8%
smtp 17871
 
3.6%
remote_job 16268
 
3.3%
pop_2 14801
 
3.0%
other 14630
 
3.0%
pm_dump 12019
 
2.4%
Other values (55) 106343
21.5%
2023-12-26T00:03:37.260675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 405384
15.3%
i 402706
15.2%
r 401758
15.1%
_ 295947
11.2%
c 284019
10.7%
t 270911
10.2%
p 193674
7.3%
a 127712
 
4.8%
v 110999
 
4.2%
h 72835
 
2.7%
Other values (28) 86934
 
3.3%
ValueCountFrequency (%)
e 334983
12.4%
r 305811
11.3%
t 303925
11.3%
p 276712
10.3%
_ 271917
10.1%
i 260322
9.7%
c 179657
 
6.7%
o 114136
 
4.2%
h 99604
 
3.7%
n 93170
 
3.5%
Other values (28) 457210
16.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2355593
88.8%
Connector Punctuation 295947
 
11.2%
Decimal Number 1107
 
< 0.1%
Uppercase Letter 232
 
< 0.1%
ValueCountFrequency (%)
Lowercase Letter 2385682
88.4%
Connector Punctuation 271917
 
10.1%
Decimal Number 39387
 
1.5%
Uppercase Letter 461
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 405384
17.2%
i 402706
17.1%
r 401758
17.1%
c 284019
12.1%
t 270911
11.5%
p 193674
8.2%
a 127712
 
5.4%
v 110999
 
4.7%
h 72835
 
3.1%
m 16722
 
0.7%
Other values (15) 68873
 
2.9%
ValueCountFrequency (%)
e 334983
14.0%
r 305811
12.8%
t 303925
12.7%
p 276712
11.6%
i 260322
10.9%
c 179657
7.5%
o 114136
 
4.8%
h 99604
 
4.2%
n 93170
 
3.9%
m 75374
 
3.2%
Other values (15) 341988
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 295947
100.0%
ValueCountFrequency (%)
_ 271917
100.0%
Decimal Number
ValueCountFrequency (%)
3 393
35.5%
4 315
28.5%
2 101
 
9.1%
5 92
 
8.3%
0 92
 
8.3%
9 92
 
8.3%
1 22
 
2.0%
ValueCountFrequency (%)
3 20377
51.7%
2 14801
37.6%
4 3753
 
9.5%
9 116
 
0.3%
5 116
 
0.3%
0 116
 
0.3%
1 108
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
Z 92
39.7%
I 43
18.5%
R 43
18.5%
C 43
18.5%
X 11
 
4.7%
ValueCountFrequency (%)
Z 116
25.2%
I 97
21.0%
R 97
21.0%
C 97
21.0%
X 54
11.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 2355825
88.8%
Common 297054
 
11.2%
ValueCountFrequency (%)
Latin 2386143
88.5%
Common 311304
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 405384
17.2%
i 402706
17.1%
r 401758
17.1%
c 284019
12.1%
t 270911
11.5%
p 193674
8.2%
a 127712
 
5.4%
v 110999
 
4.7%
h 72835
 
3.1%
m 16722
 
0.7%
Other values (20) 69105
 
2.9%
ValueCountFrequency (%)
e 334983
14.0%
r 305811
12.8%
t 303925
12.7%
p 276712
11.6%
i 260322
10.9%
c 179657
7.5%
o 114136
 
4.8%
h 99604
 
4.2%
n 93170
 
3.9%
m 75374
 
3.2%
Other values (20) 342449
14.4%
Common
ValueCountFrequency (%)
_ 295947
99.6%
3 393
 
0.1%
4 315
 
0.1%
2 101
 
< 0.1%
5 92
 
< 0.1%
0 92
 
< 0.1%
9 92
 
< 0.1%
1 22
 
< 0.1%
ValueCountFrequency (%)
_ 271917
87.3%
3 20377
 
6.5%
2 14801
 
4.8%
4 3753
 
1.2%
9 116
 
< 0.1%
5 116
 
< 0.1%
0 116
 
< 0.1%
1 108
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2652879
100.0%
ValueCountFrequency (%)
ASCII 2697447
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 405384
15.3%
i 402706
15.2%
r 401758
15.1%
_ 295947
11.2%
c 284019
10.7%
t 270911
10.2%
p 193674
7.3%
a 127712
 
4.8%
v 110999
 
4.2%
h 72835
 
2.7%
Other values (28) 86934
 
3.3%
ValueCountFrequency (%)
e 334983
12.4%
r 305811
11.3%
t 303925
11.3%
p 276712
10.3%
_ 271917
10.1%
i 260322
9.7%
c 179657
 
6.7%
o 114136
 
4.2%
h 99604
 
3.7%
n 93170
 
3.5%
Other values (28) 457210
16.9%

flag
Categorical

 RealSynthetic
Distinct1111
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
SF
378440 
S0
87007 
REJ
 
26875
RSTR
 
903
RSTO
 
579
Other values (6)
 
217
SF
276282 
S0
142130 
REJ
33679 
S3
 
19987
OTH
 
12183
Other values (6)
 
9760

Length

 RealSynthetic
Max length66
Median length22
Mean length2.06050552.1310106
Min length22

Characters and Unicode

 RealSynthetic
Total characters10179331052764
Distinct characters1212
Distinct categories22 ?
Distinct scripts22 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st rowSFS0
2nd rowSFSF
3rd rowSFSF
4th rowSFSF
5th rowS0SF

Common Values

ValueCountFrequency (%)
SF 378440
76.6%
S0 87007
 
17.6%
REJ 26875
 
5.4%
RSTR 903
 
0.2%
RSTO 579
 
0.1%
SH 107
 
< 0.1%
S1 57
 
< 0.1%
S2 24
 
< 0.1%
RSTOS0 11
 
< 0.1%
S3 10
 
< 0.1%
ValueCountFrequency (%)
SF 276282
55.9%
S0 142130
28.8%
REJ 33679
 
6.8%
S3 19987
 
4.0%
OTH 12183
 
2.5%
RSTR 6161
 
1.2%
RSTO 1145
 
0.2%
RSTOS0 1062
 
0.2%
S2 567
 
0.1%
S1 475
 
0.1%

Length

2023-12-26T00:03:37.553647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real


Number of variable categories passes threshold (config.plot.cat_freq.max_unique)

Synthetic


Number of variable categories passes threshold (config.plot.cat_freq.max_unique)
ValueCountFrequency (%)
sf 378440
76.6%
s0 87007
 
17.6%
rej 26875
 
5.4%
rstr 903
 
0.2%
rsto 579
 
0.1%
sh 107
 
< 0.1%
s1 57
 
< 0.1%
s2 24
 
< 0.1%
rstos0 11
 
< 0.1%
s3 10
 
< 0.1%
ValueCountFrequency (%)
sf 276282
55.9%
s0 142130
28.8%
rej 33679
 
6.8%
s3 19987
 
4.0%
oth 12183
 
2.5%
rstr 6161
 
1.2%
rsto 1145
 
0.2%
rstos0 1062
 
0.2%
s2 567
 
0.1%
s1 475
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 467149
45.9%
F 378440
37.2%
0 87018
 
8.5%
R 29271
 
2.9%
E 26875
 
2.6%
J 26875
 
2.6%
T 1501
 
0.1%
O 598
 
0.1%
H 115
 
< 0.1%
1 57
 
< 0.1%
Other values (2) 34
 
< 0.1%
ValueCountFrequency (%)
S 449221
42.7%
F 276282
26.2%
0 143192
 
13.6%
R 48208
 
4.6%
E 33679
 
3.2%
J 33679
 
3.2%
T 20551
 
2.0%
3 19987
 
1.9%
O 14390
 
1.4%
H 12533
 
1.2%
Other values (2) 1042
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 930824
91.4%
Decimal Number 87109
 
8.6%
ValueCountFrequency (%)
Uppercase Letter 888543
84.4%
Decimal Number 164221
 
15.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 467149
50.2%
F 378440
40.7%
R 29271
 
3.1%
E 26875
 
2.9%
J 26875
 
2.9%
T 1501
 
0.2%
O 598
 
0.1%
H 115
 
< 0.1%
ValueCountFrequency (%)
S 449221
50.6%
F 276282
31.1%
R 48208
 
5.4%
E 33679
 
3.8%
J 33679
 
3.8%
T 20551
 
2.3%
O 14390
 
1.6%
H 12533
 
1.4%
Decimal Number
ValueCountFrequency (%)
0 87018
99.9%
1 57
 
0.1%
2 24
 
< 0.1%
3 10
 
< 0.1%
ValueCountFrequency (%)
0 143192
87.2%
3 19987
 
12.2%
2 567
 
0.3%
1 475
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 930824
91.4%
Common 87109
 
8.6%
ValueCountFrequency (%)
Latin 888543
84.4%
Common 164221
 
15.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 467149
50.2%
F 378440
40.7%
R 29271
 
3.1%
E 26875
 
2.9%
J 26875
 
2.9%
T 1501
 
0.2%
O 598
 
0.1%
H 115
 
< 0.1%
ValueCountFrequency (%)
S 449221
50.6%
F 276282
31.1%
R 48208
 
5.4%
E 33679
 
3.8%
J 33679
 
3.8%
T 20551
 
2.3%
O 14390
 
1.6%
H 12533
 
1.4%
Common
ValueCountFrequency (%)
0 87018
99.9%
1 57
 
0.1%
2 24
 
< 0.1%
3 10
 
< 0.1%
ValueCountFrequency (%)
0 143192
87.2%
3 19987
 
12.2%
2 567
 
0.3%
1 475
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1017933
100.0%
ValueCountFrequency (%)
ASCII 1052764
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 467149
45.9%
F 378440
37.2%
0 87018
 
8.5%
R 29271
 
2.9%
E 26875
 
2.6%
J 26875
 
2.6%
T 1501
 
0.1%
O 598
 
0.1%
H 115
 
< 0.1%
1 57
 
< 0.1%
Other values (2) 34
 
< 0.1%
ValueCountFrequency (%)
S 449221
42.7%
F 276282
26.2%
0 143192
 
13.6%
R 48208
 
4.6%
E 33679
 
3.2%
J 33679
 
3.2%
T 20551
 
2.0%
3 19987
 
1.9%
O 14390
 
1.4%
H 12533
 
1.2%
Other values (2) 1042
 
0.1%

src_bytes
Real number (ℝ)

 RealSynthetic
Distinct330013720
Distinct (%)0.7%2.8%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean3025.610210867.997
 RealSynthetic
Minimum00
Maximum6.9337562 × 1084.6003755 × 108
Zeros115342193325
Zeros (%)23.3%39.1%
Negative00
Negative (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:37.875705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

 RealSynthetic
Minimum00
5-th percentile00
Q1450
median520237
Q31032900
95-th percentile10321443
Maximum6.9337562 × 1084.6003755 × 108
Range6.9337562 × 1084.6003755 × 108
Interquartile range (IQR)987900

Descriptive statistics

 RealSynthetic
Standard deviation988218.071220218.9
Coefficient of variation (CV)326.61777112.27634
Kurtosis490584.3569956.882
Mean3025.610210867.997
Median Absolute Deviation (MAD)512237
Skewness699.21315240.25976
Sum1.494715 × 1095.3690187 × 109
Variance9.7657495 × 10111.4889341 × 1012
MonotonicityNot monotonicNot monotonic
2023-12-26T00:03:38.245328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1032 228035
46.2%
0 115342
23.3%
520 52774
 
10.7%
105 7370
 
1.5%
147 2725
 
0.6%
54540 2143
 
0.4%
146 2033
 
0.4%
42 1069
 
0.2%
8 1045
 
0.2%
28 984
 
0.2%
Other values (3290) 80501
 
16.3%
ValueCountFrequency (%)
0 193325
39.1%
520 5998
 
1.2%
105 4583
 
0.9%
146 1785
 
0.4%
54540 1482
 
0.3%
147 1276
 
0.3%
28 1192
 
0.2%
145 1190
 
0.2%
8 1069
 
0.2%
44 931
 
0.2%
Other values (13710) 281190
56.9%
ValueCountFrequency (%)
0 115342
23.3%
1 257
 
0.1%
4 4
 
< 0.1%
5 12
 
< 0.1%
6 67
 
< 0.1%
7 104
 
< 0.1%
8 1045
 
0.2%
9 155
 
< 0.1%
10 174
 
< 0.1%
11 19
 
< 0.1%
ValueCountFrequency (%)
0 193325
39.1%
1 879
 
0.2%
2 354
 
0.1%
3 231
 
< 0.1%
4 228
 
< 0.1%
5 172
 
< 0.1%
6 258
 
0.1%
7 343
 
0.1%
8 1069
 
0.2%
9 267
 
0.1%
ValueCountFrequency (%)
0 193325
39.1%
1 879
 
0.2%
2 354
 
0.1%
3 231
 
< 0.1%
4 228
 
< 0.1%
5 172
 
< 0.1%
6 258
 
0.1%
7 343
 
0.1%
8 1069
 
0.2%
9 267
 
0.1%
ValueCountFrequency (%)
0 115342
23.3%
1 257
 
0.1%
4 4
 
< 0.1%
5 12
 
< 0.1%
6 67
 
< 0.1%
7 104
 
< 0.1%
8 1045
 
0.2%
9 155
 
< 0.1%
10 174
 
< 0.1%
11 19
 
< 0.1%

dst_bytes
Real number (ℝ)

 RealSynthetic
Distinct1072521194
Distinct (%)2.2%4.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean868.532421785.0474
 RealSynthetic
Minimum00
Maximum51554685153589
Zeros408258372649
Zeros (%)82.6%75.4%
Negative00
Negative (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:38.602738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

 RealSynthetic
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile24177034
Maximum51554685153589
Range51554685153589
Interquartile range (IQR)00

Descriptive statistics

 RealSynthetic
Standard deviation33040.00136301.847
Coefficient of variation (CV)38.04118320.336629
Kurtosis20338.14311197.148
Mean868.532421785.0474
Median Absolute Deviation (MAD)00
Skewness136.7592894.513002
Sum4.2907326 × 1088.8185088 × 108
Variance1.0916417 × 1091.3178241 × 109
MonotonicityNot monotonicNot monotonic
2023-12-26T00:03:38.958953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 408258
82.6%
105 4451
 
0.9%
147 2501
 
0.5%
146 2289
 
0.5%
8314 2133
 
0.4%
145 985
 
0.2%
42 921
 
0.2%
330 854
 
0.2%
329 804
 
0.2%
331 793
 
0.2%
Other values (10715) 70032
 
14.2%
ValueCountFrequency (%)
0 372649
75.4%
105 2729
 
0.6%
1 2099
 
0.4%
8314 1556
 
0.3%
2 1283
 
0.3%
3 857
 
0.2%
146 839
 
0.2%
147 808
 
0.2%
4 752
 
0.2%
145 668
 
0.1%
Other values (21184) 109781
 
22.2%
ValueCountFrequency (%)
0 408258
82.6%
1 5
 
< 0.1%
4 107
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
14 1
 
< 0.1%
15 5
 
< 0.1%
17 29
 
< 0.1%
18 3
 
< 0.1%
20 2
 
< 0.1%
ValueCountFrequency (%)
0 372649
75.4%
1 2099
 
0.4%
2 1283
 
0.3%
3 857
 
0.2%
4 752
 
0.2%
5 475
 
0.1%
6 373
 
0.1%
7 272
 
0.1%
8 209
 
< 0.1%
9 199
 
< 0.1%
ValueCountFrequency (%)
0 372649
75.4%
1 2099
 
0.4%
2 1283
 
0.3%
3 857
 
0.2%
4 752
 
0.2%
5 475
 
0.1%
6 373
 
0.1%
7 272
 
0.1%
8 209
 
< 0.1%
9 199
 
< 0.1%
ValueCountFrequency (%)
0 408258
82.6%
1 5
 
< 0.1%
4 107
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
14 1
 
< 0.1%
15 5
 
< 0.1%
17 29
 
< 0.1%
18 3
 
< 0.1%
20 2
 
< 0.1%

land
Categorical

 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
493999 
1
 
22
0
493996 
1
 
25

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 493999
> 99.9%
1 22
 
< 0.1%
ValueCountFrequency (%)
0 493996
> 99.9%
1 25
 
< 0.1%

Length

2023-12-26T00:03:39.241797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:39.440798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:39.599353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 493999
> 99.9%
1 22
 
< 0.1%
ValueCountFrequency (%)
0 493996
> 99.9%
1 25
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 493999
> 99.9%
1 22
 
< 0.1%
ValueCountFrequency (%)
0 493996
> 99.9%
1 25
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 493999
> 99.9%
1 22
 
< 0.1%
ValueCountFrequency (%)
0 493996
> 99.9%
1 25
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 493999
> 99.9%
1 22
 
< 0.1%
ValueCountFrequency (%)
0 493996
> 99.9%
1 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 493999
> 99.9%
1 22
 
< 0.1%
ValueCountFrequency (%)
0 493996
> 99.9%
1 25
 
< 0.1%

wrong_fragment
Categorical

 RealSynthetic
Distinct33
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
492783 
3
 
970
1
 
268
0
492103 
3
 
1454
1
 
464

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters33
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 492783
99.7%
3 970
 
0.2%
1 268
 
0.1%
ValueCountFrequency (%)
0 492103
99.6%
3 1454
 
0.3%
1 464
 
0.1%

Length

2023-12-26T00:03:39.746749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:39.954724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:40.120206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 492783
99.7%
3 970
 
0.2%
1 268
 
0.1%
ValueCountFrequency (%)
0 492103
99.6%
3 1454
 
0.3%
1 464
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 492783
99.7%
3 970
 
0.2%
1 268
 
0.1%
ValueCountFrequency (%)
0 492103
99.6%
3 1454
 
0.3%
1 464
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 492783
99.7%
3 970
 
0.2%
1 268
 
0.1%
ValueCountFrequency (%)
0 492103
99.6%
3 1454
 
0.3%
1 464
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 492783
99.7%
3 970
 
0.2%
1 268
 
0.1%
ValueCountFrequency (%)
0 492103
99.6%
3 1454
 
0.3%
1 464
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 492783
99.7%
3 970
 
0.2%
1 268
 
0.1%
ValueCountFrequency (%)
0 492103
99.6%
3 1454
 
0.3%
1 464
 
0.1%

urgent
Categorical

 RealSynthetic
Distinct42
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
494017 
1
 
2
2
 
1
3
 
1
0
494019 
3
 
2

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters42
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique20 ?
Unique (%)< 0.1%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 494017
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 494019
> 99.9%
3 2
 
< 0.1%

Length

2023-12-26T00:03:40.267146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:40.456419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:40.622186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 494017
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 494019
> 99.9%
3 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 494017
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 494019
> 99.9%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 494017
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 494019
> 99.9%
3 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 494017
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 494019
> 99.9%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 494017
> 99.9%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 494019
> 99.9%
3 2
 
< 0.1%

hot
Real number (ℝ)

 RealSynthetic
Distinct2231
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0345187760.0466215
 RealSynthetic
Minimum00
Maximum3030
Zeros490829489530
Zeros (%)99.4%99.1%
Negative00
Negative (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:40.818685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

 RealSynthetic
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum3030
Range3030
Interquartile range (IQR)00

Descriptive statistics

 RealSynthetic
Standard deviation0.782102580.87520604
Coefficient of variation (CV)22.6573118.772584
Kurtosis1127.0172795.12178
Mean0.0345187760.0466215
Median Absolute Deviation (MAD)00
Skewness32.62914527.071839
Sum1705323032
Variance0.611684450.76598562
MonotonicityNot monotonicNot monotonic
2023-12-26T00:03:41.084905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 490829
99.4%
2 2192
 
0.4%
28 274
 
0.1%
1 256
 
0.1%
4 112
 
< 0.1%
6 104
 
< 0.1%
5 51
 
< 0.1%
3 38
 
< 0.1%
14 37
 
< 0.1%
30 28
 
< 0.1%
Other values (12) 100
 
< 0.1%
ValueCountFrequency (%)
0 489530
99.1%
2 2081
 
0.4%
1 1193
 
0.2%
28 192
 
< 0.1%
4 119
 
< 0.1%
3 114
 
< 0.1%
5 97
 
< 0.1%
6 76
 
< 0.1%
7 44
 
< 0.1%
9 40
 
< 0.1%
Other values (21) 535
 
0.1%
ValueCountFrequency (%)
0 490829
99.4%
1 256
 
0.1%
2 2192
 
0.4%
3 38
 
< 0.1%
4 112
 
< 0.1%
5 51
 
< 0.1%
6 104
 
< 0.1%
7 5
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 489530
99.1%
1 1193
 
0.2%
2 2081
 
0.4%
3 114
 
< 0.1%
4 119
 
< 0.1%
5 97
 
< 0.1%
6 76
 
< 0.1%
7 44
 
< 0.1%
8 37
 
< 0.1%
9 40
 
< 0.1%
ValueCountFrequency (%)
0 489530
99.1%
1 1193
 
0.2%
2 2081
 
0.4%
3 114
 
< 0.1%
4 119
 
< 0.1%
5 97
 
< 0.1%
6 76
 
< 0.1%
7 44
 
< 0.1%
8 37
 
< 0.1%
9 40
 
< 0.1%
ValueCountFrequency (%)
0 490829
99.4%
1 256
 
0.1%
2 2192
 
0.4%
3 38
 
< 0.1%
4 112
 
< 0.1%
5 51
 
< 0.1%
6 104
 
< 0.1%
7 5
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%

num_failed_logins
Real number (ℝ)

 RealSynthetic
Distinct63
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.000151815410.00020039634
 RealSynthetic
Minimum00
Maximum52
Zeros493958493926
Zeros (%)> 99.9%> 99.9%
Negative00
Negative (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:41.302371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

 RealSynthetic
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum52
Range52
Interquartile range (IQR)00

Descriptive statistics

 RealSynthetic
Standard deviation0.0155195970.014715647
Coefficient of variation (CV)102.2267673.432713
Kurtosis37221.5976683.4983
Mean0.000151815410.00020039634
Median Absolute Deviation (MAD)00
Skewness160.8026278.090302
Sum7599
Variance0.000240857890.00021655026
MonotonicityNot monotonicNot monotonic
2023-12-26T00:03:41.471764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 493958
> 99.9%
1 57
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
0 493926
> 99.9%
1 91
 
< 0.1%
2 4
 
< 0.1%
ValueCountFrequency (%)
0 493958
> 99.9%
1 57
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 493926
> 99.9%
1 91
 
< 0.1%
2 4
 
< 0.1%
ValueCountFrequency (%)
0 493926
> 99.9%
1 91
 
< 0.1%
2 4
 
< 0.1%
ValueCountFrequency (%)
0 493958
> 99.9%
1 57
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%

logged_in
Categorical

 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
420784 
1
73237 
0
401652 
1
92369 

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row10
2nd row10
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 420784
85.2%
1 73237
 
14.8%
ValueCountFrequency (%)
0 401652
81.3%
1 92369
 
18.7%

Length

2023-12-26T00:03:41.682547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:41.886820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:42.046939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 420784
85.2%
1 73237
 
14.8%
ValueCountFrequency (%)
0 401652
81.3%
1 92369
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 420784
85.2%
1 73237
 
14.8%
ValueCountFrequency (%)
0 401652
81.3%
1 92369
 
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 420784
85.2%
1 73237
 
14.8%
ValueCountFrequency (%)
0 401652
81.3%
1 92369
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 420784
85.2%
1 73237
 
14.8%
ValueCountFrequency (%)
0 401652
81.3%
1 92369
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 420784
85.2%
1 73237
 
14.8%
ValueCountFrequency (%)
0 401652
81.3%
1 92369
 
18.7%

num_compromised
Real number (ℝ)

 RealSynthetic
Distinct2345
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0102121160.01830489
 RealSynthetic
Minimum00
Maximum884695
Zeros491797491868
Zeros (%)99.5%99.6%
Negative00
Negative (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:42.287475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

 RealSynthetic
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum884695
Range884695
Interquartile range (IQR)00

Descriptive statistics

 RealSynthetic
Standard deviation1.79832632.3373002
Coefficient of variation (CV)176.09731127.6872
Kurtosis188121.3547777.75
Mean0.0102121160.01830489
Median Absolute Deviation (MAD)00
Skewness417.53023209.45445
Sum50459043
Variance3.23397735.4629721
MonotonicityNot monotonicNot monotonic
2023-12-26T00:03:42.570392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 491797
99.5%
1 2151
 
0.4%
2 24
 
< 0.1%
4 16
 
< 0.1%
3 11
 
< 0.1%
6 3
 
< 0.1%
5 2
 
< 0.1%
7 2
 
< 0.1%
767 1
 
< 0.1%
12 1
 
< 0.1%
Other values (13) 13
 
< 0.1%
ValueCountFrequency (%)
0 491868
99.6%
1 2054
 
0.4%
2 29
 
< 0.1%
3 11
 
< 0.1%
4 7
 
< 0.1%
5 4
 
< 0.1%
9 4
 
< 0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
10 2
 
< 0.1%
Other values (35) 36
 
< 0.1%
ValueCountFrequency (%)
0 491797
99.5%
1 2151
 
0.4%
2 24
 
< 0.1%
3 11
 
< 0.1%
4 16
 
< 0.1%
5 2
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
0 491868
99.6%
1 2054
 
0.4%
2 29
 
< 0.1%
3 11
 
< 0.1%
4 7
 
< 0.1%
5 4
 
< 0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
0 491868
99.6%
1 2054
 
0.4%
2 29
 
< 0.1%
3 11
 
< 0.1%
4 7
 
< 0.1%
5 4
 
< 0.1%
6 3
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
0 491797
99.5%
1 2151
 
0.4%
2 24
 
< 0.1%
3 11
 
< 0.1%
4 16
 
< 0.1%
5 2
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%

root_shell
Categorical

 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
493966 
1
 
55
0
493982 
1
 
39

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 493966
> 99.9%
1 55
 
< 0.1%
ValueCountFrequency (%)
0 493982
> 99.9%
1 39
 
< 0.1%

Length

2023-12-26T00:03:42.786901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:42.989133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:43.141185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 493966
> 99.9%
1 55
 
< 0.1%
ValueCountFrequency (%)
0 493982
> 99.9%
1 39
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 493966
> 99.9%
1 55
 
< 0.1%
ValueCountFrequency (%)
0 493982
> 99.9%
1 39
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 493966
> 99.9%
1 55
 
< 0.1%
ValueCountFrequency (%)
0 493982
> 99.9%
1 39
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 493966
> 99.9%
1 55
 
< 0.1%
ValueCountFrequency (%)
0 493982
> 99.9%
1 39
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 493966
> 99.9%
1 55
 
< 0.1%
ValueCountFrequency (%)
0 493982
> 99.9%
1 39
 
< 0.1%

su_attempted
Categorical

 RealSynthetic
Distinct33
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
494009 
1
 
6
2
 
6
0
494011 
1
 
6
2
 
4

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters33
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 494009
> 99.9%
1 6
 
< 0.1%
2 6
 
< 0.1%
ValueCountFrequency (%)
0 494011
> 99.9%
1 6
 
< 0.1%
2 4
 
< 0.1%

Length

2023-12-26T00:03:43.290168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:43.493320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:43.653383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 494009
> 99.9%
1 6
 
< 0.1%
2 6
 
< 0.1%
ValueCountFrequency (%)
0 494011
> 99.9%
1 6
 
< 0.1%
2 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 494009
> 99.9%
1 6
 
< 0.1%
2 6
 
< 0.1%
ValueCountFrequency (%)
0 494011
> 99.9%
1 6
 
< 0.1%
2 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 494009
> 99.9%
1 6
 
< 0.1%
2 6
 
< 0.1%
ValueCountFrequency (%)
0 494011
> 99.9%
1 6
 
< 0.1%
2 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 494009
> 99.9%
1 6
 
< 0.1%
2 6
 
< 0.1%
ValueCountFrequency (%)
0 494011
> 99.9%
1 6
 
< 0.1%
2 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 494009
> 99.9%
1 6
 
< 0.1%
2 6
 
< 0.1%
ValueCountFrequency (%)
0 494011
> 99.9%
1 6
 
< 0.1%
2 4
 
< 0.1%

num_root
Real number (ℝ)

 RealSynthetic
Distinct2044
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0113517440.019646938
 RealSynthetic
Minimum00
Maximum993765
Zeros493436492697
Zeros (%)99.9%99.7%
Negative00
Negative (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:43.877641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

 RealSynthetic
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum993765
Range993765
Interquartile range (IQR)00

Descriptive statistics

 RealSynthetic
Standard deviation2.01271831.9648944
Coefficient of variation (CV)177.30476100.01021
Kurtosis188933.0580839.543
Mean0.0113517440.019646938
Median Absolute Deviation (MAD)00
Skewness417.06584260.61419
Sum56089706
Variance4.05103513.8608099
MonotonicityNot monotonicNot monotonic
2023-12-26T00:03:44.437080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 493436
99.9%
1 233
 
< 0.1%
9 167
 
< 0.1%
6 126
 
< 0.1%
2 22
 
< 0.1%
5 12
 
< 0.1%
4 10
 
< 0.1%
3 3
 
< 0.1%
12 1
 
< 0.1%
993 1
 
< 0.1%
Other values (10) 10
 
< 0.1%
ValueCountFrequency (%)
0 492697
99.7%
1 459
 
0.1%
2 210
 
< 0.1%
9 136
 
< 0.1%
3 132
 
< 0.1%
4 90
 
< 0.1%
5 73
 
< 0.1%
6 70
 
< 0.1%
7 54
 
< 0.1%
8 46
 
< 0.1%
Other values (34) 54
 
< 0.1%
ValueCountFrequency (%)
0 493436
99.9%
1 233
 
< 0.1%
2 22
 
< 0.1%
3 3
 
< 0.1%
4 10
 
< 0.1%
5 12
 
< 0.1%
6 126
 
< 0.1%
7 1
 
< 0.1%
9 167
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
0 492697
99.7%
1 459
 
0.1%
2 210
 
< 0.1%
3 132
 
< 0.1%
4 90
 
< 0.1%
5 73
 
< 0.1%
6 70
 
< 0.1%
7 54
 
< 0.1%
8 46
 
< 0.1%
9 136
 
< 0.1%
ValueCountFrequency (%)
0 492697
99.7%
1 459
 
0.1%
2 210
 
< 0.1%
3 132
 
< 0.1%
4 90
 
< 0.1%
5 73
 
< 0.1%
6 70
 
< 0.1%
7 54
 
< 0.1%
8 46
 
< 0.1%
9 136
 
< 0.1%
ValueCountFrequency (%)
0 493436
99.9%
1 233
 
< 0.1%
2 22
 
< 0.1%
3 3
 
< 0.1%
4 10
 
< 0.1%
5 12
 
< 0.1%
6 126
 
< 0.1%
7 1
 
< 0.1%
9 167
 
< 0.1%
12 1
 
< 0.1%

num_file_creations
Real number (ℝ)

 RealSynthetic
Distinct1817
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.00108294990.00089874722
 RealSynthetic
Minimum00
Maximum2825
Zeros493756493808
Zeros (%)99.9%> 99.9%
Negative00
Negative (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:44.681285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

 RealSynthetic
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum2825
Range2825
Interquartile range (IQR)00

Descriptive statistics

 RealSynthetic
Standard deviation0.0964158790.075735078
Coefficient of variation (CV)89.03078384.267386
Kurtosis43583.89542471.649
Mean0.00108294990.00089874722
Median Absolute Deviation (MAD)00
Skewness192.33477180.15833
Sum535444
Variance0.00929602170.0057358021
MonotonicityNot monotonicNot monotonic
2023-12-26T00:03:44.891190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 493756
99.9%
1 207
 
< 0.1%
2 36
 
< 0.1%
4 7
 
< 0.1%
16 2
 
< 0.1%
9 1
 
< 0.1%
5 1
 
< 0.1%
21 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
Other values (8) 8
 
< 0.1%
ValueCountFrequency (%)
0 493808
> 99.9%
1 150
 
< 0.1%
2 34
 
< 0.1%
4 7
 
< 0.1%
5 6
 
< 0.1%
3 3
 
< 0.1%
14 2
 
< 0.1%
8 2
 
< 0.1%
15 1
 
< 0.1%
11 1
 
< 0.1%
Other values (7) 7
 
< 0.1%
ValueCountFrequency (%)
0 493756
99.9%
1 207
 
< 0.1%
2 36
 
< 0.1%
4 7
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
0 493808
> 99.9%
1 150
 
< 0.1%
2 34
 
< 0.1%
3 3
 
< 0.1%
4 7
 
< 0.1%
5 6
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 493808
> 99.9%
1 150
 
< 0.1%
2 34
 
< 0.1%
3 3
 
< 0.1%
4 7
 
< 0.1%
5 6
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 493756
99.9%
1 207
 
< 0.1%
2 36
 
< 0.1%
4 7
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%

num_shells
Categorical

 RealSynthetic
Distinct33
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
493970 
1
 
48
2
 
3
0
493927 
1
 
92
2
 
2

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters33
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 493970
> 99.9%
1 48
 
< 0.1%
2 3
 
< 0.1%
ValueCountFrequency (%)
0 493927
> 99.9%
1 92
 
< 0.1%
2 2
 
< 0.1%

Length

2023-12-26T00:03:45.095786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:45.282788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:45.433614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 493970
> 99.9%
1 48
 
< 0.1%
2 3
 
< 0.1%
ValueCountFrequency (%)
0 493927
> 99.9%
1 92
 
< 0.1%
2 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 493970
> 99.9%
1 48
 
< 0.1%
2 3
 
< 0.1%
ValueCountFrequency (%)
0 493927
> 99.9%
1 92
 
< 0.1%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 493970
> 99.9%
1 48
 
< 0.1%
2 3
 
< 0.1%
ValueCountFrequency (%)
0 493927
> 99.9%
1 92
 
< 0.1%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 493970
> 99.9%
1 48
 
< 0.1%
2 3
 
< 0.1%
ValueCountFrequency (%)
0 493927
> 99.9%
1 92
 
< 0.1%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 493970
> 99.9%
1 48
 
< 0.1%
2 3
 
< 0.1%
ValueCountFrequency (%)
0 493927
> 99.9%
1 92
 
< 0.1%
2 2
 
< 0.1%

num_access_files
Real number (ℝ)

 RealSynthetic
Distinct77
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.00100805430.0015586382
 RealSynthetic
Minimum00
Maximum88
Zeros493567493323
Zeros (%)99.9%99.9%
Negative00
Negative (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:45.577895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

 RealSynthetic
Minimum00
5-th percentile00
Q100
median00
Q300
95-th percentile00
Maximum88
Range88
Interquartile range (IQR)00

Descriptive statistics

 RealSynthetic
Standard deviation0.036481690.045678823
Coefficient of variation (CV)36.19020329.30688
Kurtosis7571.40415284.8496
Mean0.00100805430.0015586382
Median Absolute Deviation (MAD)00
Skewness61.20145250.783713
Sum498770
Variance0.00133091370.0020865549
MonotonicityNot monotonicNot monotonic
2023-12-26T00:03:45.759774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 493567
99.9%
1 424
 
0.1%
2 25
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 493323
99.9%
1 648
 
0.1%
2 42
 
< 0.1%
3 4
 
< 0.1%
8 2
 
< 0.1%
6 1
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
0 493567
99.9%
1 424
 
0.1%
2 25
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 493323
99.9%
1 648
 
0.1%
2 42
 
< 0.1%
3 4
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 493323
99.9%
1 648
 
0.1%
2 42
 
< 0.1%
3 4
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 493567
99.9%
1 424
 
0.1%
2 25
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%

is_host_login
Categorical

 RealSynthetic
Distinct11
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
494021 
0
494021 

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters11
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 494021
100.0%
ValueCountFrequency (%)
0 494021
100.0%

Length

2023-12-26T00:03:45.954285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:46.131545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:46.264568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 494021
100.0%
ValueCountFrequency (%)
0 494021
100.0%

Most occurring characters

ValueCountFrequency (%)
0 494021
100.0%
ValueCountFrequency (%)
0 494021
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 494021
100.0%
ValueCountFrequency (%)
0 494021
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 494021
100.0%
ValueCountFrequency (%)
0 494021
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 494021
100.0%
ValueCountFrequency (%)
0 494021
100.0%

is_guest_login
Categorical

 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
493336 
1
 
685
0
493029 
1
 
992

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 493336
99.9%
1 685
 
0.1%
ValueCountFrequency (%)
0 493029
99.8%
1 992
 
0.2%

Length

2023-12-26T00:03:46.394087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:46.593555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:46.742152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 493336
99.9%
1 685
 
0.1%
ValueCountFrequency (%)
0 493029
99.8%
1 992
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 493336
99.9%
1 685
 
0.1%
ValueCountFrequency (%)
0 493029
99.8%
1 992
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 493336
99.9%
1 685
 
0.1%
ValueCountFrequency (%)
0 493029
99.8%
1 992
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 493336
99.9%
1 685
 
0.1%
ValueCountFrequency (%)
0 493029
99.8%
1 992
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 493336
99.9%
1 685
 
0.1%
ValueCountFrequency (%)
0 493029
99.8%
1 992
 
0.2%

count
Real number (ℝ)

 RealSynthetic
Distinct490512
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean332.28569248.55286
 RealSynthetic
Minimum00
Maximum511511
Zeros29930
Zeros (%)< 0.1%2.0%
Negative00
Negative (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:46.984709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

 RealSynthetic
Minimum00
5-th percentile11
Q111722
median510216
Q3511492
95-th percentile511509
Maximum511511
Range511511
Interquartile range (IQR)394470

Descriptive statistics

 RealSynthetic
Standard deviation213.14741200.59989
Coefficient of variation (CV)0.641458290.80707134
Kurtosis-1.4722098-1.5650079
Mean332.28569248.55286
Median Absolute Deviation (MAD)1213
Skewness-0.542005620.14643106
Sum1.6415611 × 1081.2279033 × 108
Variance45431.81940240.315
MonotonicityNot monotonicNot monotonic
2023-12-26T00:03:47.331480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
511 227895
46.1%
1 39214
 
7.9%
510 26598
 
5.4%
2 11219
 
2.3%
3 5812
 
1.2%
509 5605
 
1.1%
4 5400
 
1.1%
5 4469
 
0.9%
6 3708
 
0.8%
7 3254
 
0.7%
Other values (480) 160847
32.6%
ValueCountFrequency (%)
1 46332
 
9.4%
2 11932
 
2.4%
0 9930
 
2.0%
511 8609
 
1.7%
509 8584
 
1.7%
510 8578
 
1.7%
508 7989
 
1.6%
507 7657
 
1.5%
3 7093
 
1.4%
506 7084
 
1.4%
Other values (502) 370233
74.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 39214
7.9%
2 11219
 
2.3%
3 5812
 
1.2%
4 5400
 
1.1%
5 4469
 
0.9%
6 3708
 
0.8%
7 3254
 
0.7%
8 2921
 
0.6%
9 2692
 
0.5%
ValueCountFrequency (%)
0 9930
 
2.0%
1 46332
9.4%
2 11932
 
2.4%
3 7093
 
1.4%
4 6303
 
1.3%
5 5171
 
1.0%
6 4304
 
0.9%
7 3865
 
0.8%
8 3543
 
0.7%
9 3101
 
0.6%
ValueCountFrequency (%)
0 9930
 
2.0%
1 46332
9.4%
2 11932
 
2.4%
3 7093
 
1.4%
4 6303
 
1.3%
5 5171
 
1.0%
6 4304
 
0.9%
7 3865
 
0.8%
8 3543
 
0.7%
9 3101
 
0.6%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 39214
7.9%
2 11219
 
2.3%
3 5812
 
1.2%
4 5400
 
1.1%
5 4469
 
0.9%
6 3708
 
0.8%
7 3254
 
0.7%
8 2921
 
0.6%
9 2692
 
0.5%

srv_count
Real number (ℝ)

 RealSynthetic
Distinct470494
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean292.90656180.1871
 RealSynthetic
Minimum00
Maximum511511
Zeros211957
Zeros (%)< 0.1%2.4%
Negative00
Negative (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:47.675420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

 RealSynthetic
Minimum00
5-th percentile11
Q1106
median51016
Q3511492
95-th percentile511509
Maximum511511
Range511511
Interquartile range (IQR)501486

Descriptive statistics

 RealSynthetic
Standard deviation246.32282231.61449
Coefficient of variation (CV)0.840960411.285411
Kurtosis-1.9151417-1.5886243
Mean292.90656180.1871
Median Absolute Deviation (MAD)114
Skewness-0.273847930.63085013
Sum1.4470199 × 10889016213
Variance60674.9353645.271
MonotonicityNot monotonicNot monotonic
2023-12-26T00:03:48.041957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
511 226559
45.9%
1 37001
 
7.5%
510 26898
 
5.4%
2 18857
 
3.8%
3 11280
 
2.3%
4 9875
 
2.0%
5 9160
 
1.9%
6 8615
 
1.7%
8 8150
 
1.6%
7 7982
 
1.6%
Other values (460) 129644
26.2%
ValueCountFrequency (%)
1 45033
 
9.1%
2 22930
 
4.6%
3 15664
 
3.2%
4 14130
 
2.9%
5 13554
 
2.7%
9 12926
 
2.6%
10 12856
 
2.6%
11 12790
 
2.6%
8 12746
 
2.6%
6 12439
 
2.5%
Other values (484) 318953
64.6%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 37001
7.5%
2 18857
3.8%
3 11280
 
2.3%
4 9875
 
2.0%
5 9160
 
1.9%
6 8615
 
1.7%
7 7982
 
1.6%
8 8150
 
1.6%
9 7557
 
1.5%
ValueCountFrequency (%)
0 11957
 
2.4%
1 45033
9.1%
2 22930
4.6%
3 15664
 
3.2%
4 14130
 
2.9%
5 13554
 
2.7%
6 12439
 
2.5%
7 12043
 
2.4%
8 12746
 
2.6%
9 12926
 
2.6%
ValueCountFrequency (%)
0 11957
 
2.4%
1 45033
9.1%
2 22930
4.6%
3 15664
 
3.2%
4 14130
 
2.9%
5 13554
 
2.7%
6 12439
 
2.5%
7 12043
 
2.4%
8 12746
 
2.6%
9 12926
 
2.6%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 37001
7.5%
2 18857
3.8%
3 11280
 
2.3%
4 9875
 
2.0%
5 9160
 
1.9%
6 8615
 
1.7%
7 7982
 
1.6%
8 8150
 
1.6%
9 7557
 
1.5%

serror_rate
Categorical

 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
407484 
1
86537 
0
346415 
1
147606 

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row01
2nd row00
3rd row00
4th row00
5th row10

Common Values

ValueCountFrequency (%)
0 407484
82.5%
1 86537
 
17.5%
ValueCountFrequency (%)
0 346415
70.1%
1 147606
29.9%

Length

2023-12-26T00:03:48.311435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:48.484291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:48.633983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 407484
82.5%
1 86537
 
17.5%
ValueCountFrequency (%)
0 346415
70.1%
1 147606
29.9%

Most occurring characters

ValueCountFrequency (%)
0 407484
82.5%
1 86537
 
17.5%
ValueCountFrequency (%)
0 346415
70.1%
1 147606
29.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 407484
82.5%
1 86537
 
17.5%
ValueCountFrequency (%)
0 346415
70.1%
1 147606
29.9%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 407484
82.5%
1 86537
 
17.5%
ValueCountFrequency (%)
0 346415
70.1%
1 147606
29.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 407484
82.5%
1 86537
 
17.5%
ValueCountFrequency (%)
0 346415
70.1%
1 147606
29.9%

srv_serror_rate
Categorical

 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
406969 
1
87052 
0
345573 
1
148448 

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row01
2nd row00
3rd row00
4th row00
5th row10

Common Values

ValueCountFrequency (%)
0 406969
82.4%
1 87052
 
17.6%
ValueCountFrequency (%)
0 345573
70.0%
1 148448
30.0%

Length

2023-12-26T00:03:48.800879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:48.995231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:49.150910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 406969
82.4%
1 87052
 
17.6%
ValueCountFrequency (%)
0 345573
70.0%
1 148448
30.0%

Most occurring characters

ValueCountFrequency (%)
0 406969
82.4%
1 87052
 
17.6%
ValueCountFrequency (%)
0 345573
70.0%
1 148448
30.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 406969
82.4%
1 87052
 
17.6%
ValueCountFrequency (%)
0 345573
70.0%
1 148448
30.0%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 406969
82.4%
1 87052
 
17.6%
ValueCountFrequency (%)
0 345573
70.0%
1 148448
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 406969
82.4%
1 87052
 
17.6%
ValueCountFrequency (%)
0 345573
70.0%
1 148448
30.0%

rerror_rate
Categorical

 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
467042 
1
 
26979
0
447573 
1
46448 

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 467042
94.5%
1 26979
 
5.5%
ValueCountFrequency (%)
0 447573
90.6%
1 46448
 
9.4%

Length

2023-12-26T00:03:49.305857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:49.484074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:49.639586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 467042
94.5%
1 26979
 
5.5%
ValueCountFrequency (%)
0 447573
90.6%
1 46448
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0 467042
94.5%
1 26979
 
5.5%
ValueCountFrequency (%)
0 447573
90.6%
1 46448
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 467042
94.5%
1 26979
 
5.5%
ValueCountFrequency (%)
0 447573
90.6%
1 46448
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 467042
94.5%
1 26979
 
5.5%
ValueCountFrequency (%)
0 447573
90.6%
1 46448
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 467042
94.5%
1 26979
 
5.5%
ValueCountFrequency (%)
0 447573
90.6%
1 46448
 
9.4%

srv_rerror_rate
Categorical

 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
465905 
1
 
28116
0
446364 
1
47657 

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 465905
94.3%
1 28116
 
5.7%
ValueCountFrequency (%)
0 446364
90.4%
1 47657
 
9.6%

Length

2023-12-26T00:03:49.795479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:49.972533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:50.115597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 465905
94.3%
1 28116
 
5.7%
ValueCountFrequency (%)
0 446364
90.4%
1 47657
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 465905
94.3%
1 28116
 
5.7%
ValueCountFrequency (%)
0 446364
90.4%
1 47657
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 465905
94.3%
1 28116
 
5.7%
ValueCountFrequency (%)
0 446364
90.4%
1 47657
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 465905
94.3%
1 28116
 
5.7%
ValueCountFrequency (%)
0 446364
90.4%
1 47657
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 465905
94.3%
1 28116
 
5.7%
ValueCountFrequency (%)
0 446364
90.4%
1 47657
 
9.6%

same_srv_rate
Categorical

 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
1
382079 
0
111942 
1
302228 
0
191793 

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row10
2nd row11
3rd row11
4th row11
5th row01

Common Values

ValueCountFrequency (%)
1 382079
77.3%
0 111942
 
22.7%
ValueCountFrequency (%)
1 302228
61.2%
0 191793
38.8%

Length

2023-12-26T00:03:50.260673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:50.455284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:50.611186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 382079
77.3%
0 111942
 
22.7%
ValueCountFrequency (%)
1 302228
61.2%
0 191793
38.8%

Most occurring characters

ValueCountFrequency (%)
1 382079
77.3%
0 111942
 
22.7%
ValueCountFrequency (%)
1 302228
61.2%
0 191793
38.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 382079
77.3%
0 111942
 
22.7%
ValueCountFrequency (%)
1 302228
61.2%
0 191793
38.8%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 382079
77.3%
0 111942
 
22.7%
ValueCountFrequency (%)
1 302228
61.2%
0 191793
38.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 382079
77.3%
0 111942
 
22.7%
ValueCountFrequency (%)
1 302228
61.2%
0 191793
38.8%

diff_srv_rate
Categorical

 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
491663 
1
 
2358
0
491286 
1
 
2735

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 491663
99.5%
1 2358
 
0.5%
ValueCountFrequency (%)
0 491286
99.4%
1 2735
 
0.6%

Length

2023-12-26T00:03:50.774439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:50.963487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:51.109116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 491663
99.5%
1 2358
 
0.5%
ValueCountFrequency (%)
0 491286
99.4%
1 2735
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 491663
99.5%
1 2358
 
0.5%
ValueCountFrequency (%)
0 491286
99.4%
1 2735
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 491663
99.5%
1 2358
 
0.5%
ValueCountFrequency (%)
0 491286
99.4%
1 2735
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 491663
99.5%
1 2358
 
0.5%
ValueCountFrequency (%)
0 491286
99.4%
1 2735
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 491663
99.5%
1 2358
 
0.5%
ValueCountFrequency (%)
0 491286
99.4%
1 2735
 
0.6%
 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
485922 
1
 
8099
0
480788 
1
 
13233

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 485922
98.4%
1 8099
 
1.6%
ValueCountFrequency (%)
0 480788
97.3%
1 13233
 
2.7%

Length

2023-12-26T00:03:51.251288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:51.425724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:51.576249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 485922
98.4%
1 8099
 
1.6%
ValueCountFrequency (%)
0 480788
97.3%
1 13233
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 485922
98.4%
1 8099
 
1.6%
ValueCountFrequency (%)
0 480788
97.3%
1 13233
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 485922
98.4%
1 8099
 
1.6%
ValueCountFrequency (%)
0 480788
97.3%
1 13233
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 485922
98.4%
1 8099
 
1.6%
ValueCountFrequency (%)
0 480788
97.3%
1 13233
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 485922
98.4%
1 8099
 
1.6%
ValueCountFrequency (%)
0 480788
97.3%
1 13233
 
2.7%

dst_host_count
Real number (ℝ)

 RealSynthetic
Distinct256256
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean232.47078222.87339
 RealSynthetic
Minimum00
Maximum255255
Zeros31042
Zeros (%)< 0.1%0.2%
Negative00
Negative (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:51.822882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

 RealSynthetic
Minimum00
5-th percentile3332
Q1255248
median255254
Q3255255
95-th percentile255255
Maximum255255
Range255255
Interquartile range (IQR)07

Descriptive statistics

 RealSynthetic
Standard deviation64.7453869.319342
Coefficient of variation (CV)0.278509760.31102565
Kurtosis5.8528673.1571467
Mean232.47078222.87339
Median Absolute Deviation (MAD)01
Skewness-2.7306878-2.1470775
Sum1.1484545 × 1081.1010414 × 108
Variance4191.96434805.1712
MonotonicityNot monotonicNot monotonic
2023-12-26T00:03:52.169522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 432829
87.6%
1 2884
 
0.6%
2 2023
 
0.4%
3 1434
 
0.3%
4 1317
 
0.3%
5 1073
 
0.2%
6 1007
 
0.2%
7 866
 
0.2%
8 842
 
0.2%
9 814
 
0.2%
Other values (246) 48932
 
9.9%
ValueCountFrequency (%)
255 222115
45.0%
254 75956
 
15.4%
253 24855
 
5.0%
252 15883
 
3.2%
251 11558
 
2.3%
250 8950
 
1.8%
249 7036
 
1.4%
248 5443
 
1.1%
247 4248
 
0.9%
246 3367
 
0.7%
Other values (246) 114610
23.2%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 2884
0.6%
2 2023
0.4%
3 1434
0.3%
4 1317
0.3%
5 1073
 
0.2%
6 1007
 
0.2%
7 866
 
0.2%
8 842
 
0.2%
9 814
 
0.2%
ValueCountFrequency (%)
0 1042
 
0.2%
1 2850
0.6%
2 1418
0.3%
3 1059
 
0.2%
4 1045
 
0.2%
5 862
 
0.2%
6 892
 
0.2%
7 844
 
0.2%
8 836
 
0.2%
9 815
 
0.2%
ValueCountFrequency (%)
0 1042
 
0.2%
1 2850
0.6%
2 1418
0.3%
3 1059
 
0.2%
4 1045
 
0.2%
5 862
 
0.2%
6 892
 
0.2%
7 844
 
0.2%
8 836
 
0.2%
9 815
 
0.2%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 2884
0.6%
2 2023
0.4%
3 1434
0.3%
4 1317
0.3%
5 1073
 
0.2%
6 1007
 
0.2%
7 866
 
0.2%
8 842
 
0.2%
9 814
 
0.2%

dst_host_srv_count
Real number (ℝ)

 RealSynthetic
Distinct256256
Distinct (%)0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean188.66567142.11465
 RealSynthetic
Minimum00
Maximum255255
Zeros34562
Zeros (%)< 0.1%0.9%
Negative00
Negative (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
2023-12-26T00:03:52.490605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

 RealSynthetic
Minimum00
5-th percentile32
Q14613
median255212
Q3255255
95-th percentile255255
Maximum255255
Range255255
Interquartile range (IQR)209242

Descriptive statistics

 RealSynthetic
Standard deviation106.04044115.83903
Coefficient of variation (CV)0.562054760.81510972
Kurtosis-0.8741455-1.8885316
Mean188.66567142.11465
Median Absolute Deviation (MAD)043
Skewness-1.0348572-0.18025984
Sum9320480370207622
Variance11244.57413418.682
MonotonicityNot monotonicNot monotonic
2023-12-26T00:03:52.862612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 337746
68.4%
1 11895
 
2.4%
2 7243
 
1.5%
3 5855
 
1.2%
11 5627
 
1.1%
8 5579
 
1.1%
10 5550
 
1.1%
5 5494
 
1.1%
6 5394
 
1.1%
12 5388
 
1.1%
Other values (246) 98250
 
19.9%
ValueCountFrequency (%)
255 131968
26.7%
254 50449
 
10.2%
253 14594
 
3.0%
1 13612
 
2.8%
10 10596
 
2.1%
11 10559
 
2.1%
8 10190
 
2.1%
12 10188
 
2.1%
9 10175
 
2.1%
13 9868
 
2.0%
Other values (246) 221822
44.9%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 11895
2.4%
2 7243
1.5%
3 5855
1.2%
4 5382
1.1%
5 5494
1.1%
6 5394
1.1%
7 5265
1.1%
8 5579
1.1%
9 5261
1.1%
ValueCountFrequency (%)
0 4562
 
0.9%
1 13612
2.8%
2 8767
1.8%
3 8505
1.7%
4 8843
1.8%
5 9331
1.9%
6 8840
1.8%
7 9315
1.9%
8 10190
2.1%
9 10175
2.1%
ValueCountFrequency (%)
0 4562
 
0.9%
1 13612
2.8%
2 8767
1.8%
3 8505
1.7%
4 8843
1.8%
5 9331
1.9%
6 8840
1.8%
7 9315
1.9%
8 10190
2.1%
9 10175
2.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 11895
2.4%
2 7243
1.5%
3 5855
1.2%
4 5382
1.1%
5 5494
1.1%
6 5394
1.1%
7 5265
1.1%
8 5579
1.1%
9 5261
1.1%
 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
1
347828 
0
146193 
1
253035 
0
240986 

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row10
3rd row11
4th row11
5th row01

Common Values

ValueCountFrequency (%)
1 347828
70.4%
0 146193
29.6%
ValueCountFrequency (%)
1 253035
51.2%
0 240986
48.8%

Length

2023-12-26T00:03:53.124516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:53.309473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:53.461909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 347828
70.4%
0 146193
29.6%
ValueCountFrequency (%)
1 253035
51.2%
0 240986
48.8%

Most occurring characters

ValueCountFrequency (%)
1 347828
70.4%
0 146193
29.6%
ValueCountFrequency (%)
1 253035
51.2%
0 240986
48.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 347828
70.4%
0 146193
29.6%
ValueCountFrequency (%)
1 253035
51.2%
0 240986
48.8%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 347828
70.4%
0 146193
29.6%
ValueCountFrequency (%)
1 253035
51.2%
0 240986
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 347828
70.4%
0 146193
29.6%
ValueCountFrequency (%)
1 253035
51.2%
0 240986
48.8%
 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
492008 
1
 
2013
0
491867 
1
 
2154

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 492008
99.6%
1 2013
 
0.4%
ValueCountFrequency (%)
0 491867
99.6%
1 2154
 
0.4%

Length

2023-12-26T00:03:53.607845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:53.784739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:53.928530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 492008
99.6%
1 2013
 
0.4%
ValueCountFrequency (%)
0 491867
99.6%
1 2154
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 492008
99.6%
1 2013
 
0.4%
ValueCountFrequency (%)
0 491867
99.6%
1 2154
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 492008
99.6%
1 2013
 
0.4%
ValueCountFrequency (%)
0 491867
99.6%
1 2154
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 492008
99.6%
1 2013
 
0.4%
ValueCountFrequency (%)
0 491867
99.6%
1 2154
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 492008
99.6%
1 2013
 
0.4%
ValueCountFrequency (%)
0 491867
99.6%
1 2154
 
0.4%
 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
1
288883 
0
205138 
0
310834 
1
183187 

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row11
4th row11
5th row01

Common Values

ValueCountFrequency (%)
1 288883
58.5%
0 205138
41.5%
ValueCountFrequency (%)
0 310834
62.9%
1 183187
37.1%

Length

2023-12-26T00:03:54.076553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:54.266815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:54.429243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 288883
58.5%
0 205138
41.5%
ValueCountFrequency (%)
0 310834
62.9%
1 183187
37.1%

Most occurring characters

ValueCountFrequency (%)
1 288883
58.5%
0 205138
41.5%
ValueCountFrequency (%)
0 310834
62.9%
1 183187
37.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 288883
58.5%
0 205138
41.5%
ValueCountFrequency (%)
0 310834
62.9%
1 183187
37.1%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 288883
58.5%
0 205138
41.5%
ValueCountFrequency (%)
0 310834
62.9%
1 183187
37.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 288883
58.5%
0 205138
41.5%
ValueCountFrequency (%)
0 310834
62.9%
1 183187
37.1%
 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
493667 
1
 
354
0
493512 
1
 
509

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 493667
99.9%
1 354
 
0.1%
ValueCountFrequency (%)
0 493512
99.9%
1 509
 
0.1%

Length

2023-12-26T00:03:54.569665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:54.758348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:54.902802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 493667
99.9%
1 354
 
0.1%
ValueCountFrequency (%)
0 493512
99.9%
1 509
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 493667
99.9%
1 354
 
0.1%
ValueCountFrequency (%)
0 493512
99.9%
1 509
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 493667
99.9%
1 354
 
0.1%
ValueCountFrequency (%)
0 493512
99.9%
1 509
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 493667
99.9%
1 354
 
0.1%
ValueCountFrequency (%)
0 493512
99.9%
1 509
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 493667
99.9%
1 354
 
0.1%
ValueCountFrequency (%)
0 493512
99.9%
1 509
 
0.1%
 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
407262 
1
86759 
0
345939 
1
148082 

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row01
2nd row00
3rd row00
4th row00
5th row10

Common Values

ValueCountFrequency (%)
0 407262
82.4%
1 86759
 
17.6%
ValueCountFrequency (%)
0 345939
70.0%
1 148082
30.0%

Length

2023-12-26T00:03:55.056478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:55.236781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:55.390471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 407262
82.4%
1 86759
 
17.6%
ValueCountFrequency (%)
0 345939
70.0%
1 148082
30.0%

Most occurring characters

ValueCountFrequency (%)
0 407262
82.4%
1 86759
 
17.6%
ValueCountFrequency (%)
0 345939
70.0%
1 148082
30.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 407262
82.4%
1 86759
 
17.6%
ValueCountFrequency (%)
0 345939
70.0%
1 148082
30.0%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 407262
82.4%
1 86759
 
17.6%
ValueCountFrequency (%)
0 345939
70.0%
1 148082
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 407262
82.4%
1 86759
 
17.6%
ValueCountFrequency (%)
0 345939
70.0%
1 148082
30.0%
 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
407024 
1
86997 
0
345547 
1
148474 

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row01
2nd row00
3rd row00
4th row00
5th row10

Common Values

ValueCountFrequency (%)
0 407024
82.4%
1 86997
 
17.6%
ValueCountFrequency (%)
0 345547
69.9%
1 148474
30.1%

Length

2023-12-26T00:03:55.542373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:55.715576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:55.863799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 407024
82.4%
1 86997
 
17.6%
ValueCountFrequency (%)
0 345547
69.9%
1 148474
30.1%

Most occurring characters

ValueCountFrequency (%)
0 407024
82.4%
1 86997
 
17.6%
ValueCountFrequency (%)
0 345547
69.9%
1 148474
30.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 407024
82.4%
1 86997
 
17.6%
ValueCountFrequency (%)
0 345547
69.9%
1 148474
30.1%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 407024
82.4%
1 86997
 
17.6%
ValueCountFrequency (%)
0 345547
69.9%
1 148474
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 407024
82.4%
1 86997
 
17.6%
ValueCountFrequency (%)
0 345547
69.9%
1 148474
30.1%
 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
467981 
1
 
26040
0
448913 
1
45108 

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 467981
94.7%
1 26040
 
5.3%
ValueCountFrequency (%)
0 448913
90.9%
1 45108
 
9.1%

Length

2023-12-26T00:03:56.014521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:56.200797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:56.361966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 467981
94.7%
1 26040
 
5.3%
ValueCountFrequency (%)
0 448913
90.9%
1 45108
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 467981
94.7%
1 26040
 
5.3%
ValueCountFrequency (%)
0 448913
90.9%
1 45108
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 467981
94.7%
1 26040
 
5.3%
ValueCountFrequency (%)
0 448913
90.9%
1 45108
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 467981
94.7%
1 26040
 
5.3%
ValueCountFrequency (%)
0 448913
90.9%
1 45108
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 467981
94.7%
1 26040
 
5.3%
ValueCountFrequency (%)
0 448913
90.9%
1 45108
 
9.1%
 RealSynthetic
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
0
468326 
1
 
25695
0
449767 
1
 
44254

Length

 RealSynthetic
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 RealSynthetic
Total characters494021494021
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st row00
2nd row00
3rd row00
4th row00
5th row00

Common Values

ValueCountFrequency (%)
0 468326
94.8%
1 25695
 
5.2%
ValueCountFrequency (%)
0 449767
91.0%
1 44254
 
9.0%

Length

2023-12-26T00:03:56.759081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:56.928698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:57.081046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 468326
94.8%
1 25695
 
5.2%
ValueCountFrequency (%)
0 449767
91.0%
1 44254
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 468326
94.8%
1 25695
 
5.2%
ValueCountFrequency (%)
0 449767
91.0%
1 44254
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494021
100.0%
ValueCountFrequency (%)
Decimal Number 494021
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 468326
94.8%
1 25695
 
5.2%
ValueCountFrequency (%)
0 449767
91.0%
1 44254
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 494021
100.0%
ValueCountFrequency (%)
Common 494021
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 468326
94.8%
1 25695
 
5.2%
ValueCountFrequency (%)
0 449767
91.0%
1 44254
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 494021
100.0%
ValueCountFrequency (%)
ASCII 494021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 468326
94.8%
1 25695
 
5.2%
ValueCountFrequency (%)
0 449767
91.0%
1 44254
 
9.0%

label
Categorical

 RealSynthetic
Distinct55
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size3.8 MiB3.8 MiB
dos
391458 
normal
97278 
probe
 
4107
r2l
 
1126
u2r
 
52
dos
363308 
normal
124050 
probe
 
5089
r2l
 
1528
u2r
 
46

Length

 RealSynthetic
Max length66
Median length33
Mean length3.60735883.7739104
Min length33

Characters and Unicode

 RealSynthetic
Total characters17821111864391
Distinct characters1313
Distinct categories22 ?
Distinct scripts22 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 RealSynthetic
Unique00 ?
Unique (%)0.0%0.0%

Sample

 RealSynthetic
1st rownormaldos
2nd rownormalnormal
3rd rowdosdos
4th rowdosdos
5th rowdosdos

Common Values

ValueCountFrequency (%)
dos 391458
79.2%
normal 97278
 
19.7%
probe 4107
 
0.8%
r2l 1126
 
0.2%
u2r 52
 
< 0.1%
ValueCountFrequency (%)
dos 363308
73.5%
normal 124050
 
25.1%
probe 5089
 
1.0%
r2l 1528
 
0.3%
u2r 46
 
< 0.1%

Length

2023-12-26T00:03:57.237779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Real

2023-12-26T00:03:57.438704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:57.621962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
dos 391458
79.2%
normal 97278
 
19.7%
probe 4107
 
0.8%
r2l 1126
 
0.2%
u2r 52
 
< 0.1%
ValueCountFrequency (%)
dos 363308
73.5%
normal 124050
 
25.1%
probe 5089
 
1.0%
r2l 1528
 
0.3%
u2r 46
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 492843
27.7%
d 391458
22.0%
s 391458
22.0%
r 102563
 
5.8%
l 98404
 
5.5%
n 97278
 
5.5%
m 97278
 
5.5%
a 97278
 
5.5%
p 4107
 
0.2%
b 4107
 
0.2%
Other values (3) 5337
 
0.3%
ValueCountFrequency (%)
o 492447
26.4%
d 363308
19.5%
s 363308
19.5%
r 130713
 
7.0%
l 125578
 
6.7%
n 124050
 
6.7%
m 124050
 
6.7%
a 124050
 
6.7%
p 5089
 
0.3%
b 5089
 
0.3%
Other values (3) 6709
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1780933
99.9%
Decimal Number 1178
 
0.1%
ValueCountFrequency (%)
Lowercase Letter 1862817
99.9%
Decimal Number 1574
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 492843
27.7%
d 391458
22.0%
s 391458
22.0%
r 102563
 
5.8%
l 98404
 
5.5%
n 97278
 
5.5%
m 97278
 
5.5%
a 97278
 
5.5%
p 4107
 
0.2%
b 4107
 
0.2%
Other values (2) 4159
 
0.2%
ValueCountFrequency (%)
o 492447
26.4%
d 363308
19.5%
s 363308
19.5%
r 130713
 
7.0%
l 125578
 
6.7%
n 124050
 
6.7%
m 124050
 
6.7%
a 124050
 
6.7%
p 5089
 
0.3%
b 5089
 
0.3%
Other values (2) 5135
 
0.3%
Decimal Number
ValueCountFrequency (%)
2 1178
100.0%
ValueCountFrequency (%)
2 1574
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1780933
99.9%
Common 1178
 
0.1%
ValueCountFrequency (%)
Latin 1862817
99.9%
Common 1574
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 492843
27.7%
d 391458
22.0%
s 391458
22.0%
r 102563
 
5.8%
l 98404
 
5.5%
n 97278
 
5.5%
m 97278
 
5.5%
a 97278
 
5.5%
p 4107
 
0.2%
b 4107
 
0.2%
Other values (2) 4159
 
0.2%
ValueCountFrequency (%)
o 492447
26.4%
d 363308
19.5%
s 363308
19.5%
r 130713
 
7.0%
l 125578
 
6.7%
n 124050
 
6.7%
m 124050
 
6.7%
a 124050
 
6.7%
p 5089
 
0.3%
b 5089
 
0.3%
Other values (2) 5135
 
0.3%
Common
ValueCountFrequency (%)
2 1178
100.0%
ValueCountFrequency (%)
2 1574
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1782111
100.0%
ValueCountFrequency (%)
ASCII 1864391
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 492843
27.7%
d 391458
22.0%
s 391458
22.0%
r 102563
 
5.8%
l 98404
 
5.5%
n 97278
 
5.5%
m 97278
 
5.5%
a 97278
 
5.5%
p 4107
 
0.2%
b 4107
 
0.2%
Other values (3) 5337
 
0.3%
ValueCountFrequency (%)
o 492447
26.4%
d 363308
19.5%
s 363308
19.5%
r 130713
 
7.0%
l 125578
 
6.7%
n 124050
 
6.7%
m 124050
 
6.7%
a 124050
 
6.7%
p 5089
 
0.3%
b 5089
 
0.3%
Other values (3) 6709
 
0.4%

Interactions

Real

2023-12-26T00:01:07.658910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:17.265968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:15.270690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:18.600913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:19.267801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:22.395512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:23.432276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:26.160490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:27.364670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:29.861053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:31.833822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:33.676278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:35.955467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:38.005026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:39.808014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:42.568127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:43.743109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:47.310909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:47.931081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:52.051533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:51.938293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:56.972093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:56.083679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:02.357079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:59.995305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:07.228246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:03.825814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:12.244946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:07.943288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:17.681231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:15.613849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:18.857996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:19.528811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:22.652898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:23.716527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:26.413290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:27.631115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:30.159274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:32.159833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:33.927312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:36.237826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:38.315961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:40.074333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:42.902535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:44.018966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:47.633717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:48.214174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:52.369302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:52.233838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:57.339054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:56.371849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:02.656455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:00.264141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:07.604765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:04.097584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:12.580595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:08.216298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:18.017717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:15.907144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:19.112125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:19.796606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:22.923815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:24.004985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:26.664456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:27.965425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:30.425898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:32.477634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:34.515577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:36.527287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:38.645297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:40.343335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:43.199534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:44.294034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:47.911101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:48.499960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:52.701304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:52.555837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:57.704267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:56.644631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:02.987700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:00.561259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:07.967083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:04.366625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:12.924972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:08.513050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:18.395288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:16.202014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:19.365514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:20.084851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:23.169122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:24.294455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:26.936936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:28.301176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:30.670044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:32.802725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:34.777287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:36.816280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:38.947132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:40.625926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:43.513150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:44.567444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:48.218096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:48.792083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:53.032069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:52.881032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:58.042928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:56.916052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:03.329778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:00.839275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:08.311311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:04.660394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:13.258888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:08.786048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:18.740137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:16.462610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:19.626113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:20.375820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:23.432006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:24.569881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:27.200379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:28.614424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:30.934092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:33.108937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:35.028997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:37.084232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:39.267954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:40.912349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:43.864947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:44.872433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:48.541174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:49.082269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:53.393033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:53.189283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:58.388883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:57.192080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:03.672965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:01.107264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:08.649749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:04.933526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:13.623423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:09.048698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:19.107789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:16.750995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:19.899513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:20.658147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:23.703110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

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Synthetic

2023-12-26T00:03:06.207269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:03.023374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:11.173043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:06.861797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:16.165532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:11.215394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:21.926817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:18.691082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:21.855916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:22.854474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:25.617801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:26.816074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:29.303381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:31.204086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:33.134939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:35.386645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:37.413419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:39.253342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:41.894617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:43.182769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:46.615270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:47.374918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:51.314506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:51.368998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:56.288596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:55.498499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:01.632891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:59.428991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:06.550828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:03.285262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:11.513977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:07.121531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:16.537151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:11.476545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:22.295436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:18.985266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:22.138728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:23.135296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:25.886267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:27.094713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:29.563925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:31.495914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:33.410363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:35.682488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:37.706573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:39.533250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:42.248549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:43.469668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:46.960617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:47.654449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:51.676876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:51.653197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:02:56.627104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:55.793130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:02.002865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:00:59.705987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:06.893511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:03.562671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:11.868714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

2023-12-26T00:01:07.392575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:16.908110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

Real

2023-12-26T00:03:57.874117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Synthetic

2023-12-26T00:03:58.553816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Real

Unnamed: 0durationsrc_bytesdst_byteshotnum_failed_loginsnum_compromisednum_rootnum_file_creationsnum_access_filescountsrv_countdst_host_countdst_host_srv_countprotocol_typeflaglandwrong_fragmenturgentlogged_inroot_shellsu_attemptednum_shellsis_guest_loginserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratelabel
Unnamed: 01.000-0.0010.001-0.0010.0010.0020.0000.0000.0000.0020.0010.001-0.0010.0000.0000.0010.0000.0000.0000.0000.0010.0000.0030.0000.0000.0000.0040.0040.0000.0000.0040.0020.0000.0000.0020.0000.0000.0040.0040.000
duration-0.0011.0000.0140.2990.1090.0140.0110.0130.0610.019-0.259-0.250-0.161-0.2170.1410.0800.0000.0000.0000.0110.0310.0600.0000.0270.0210.0220.0370.0360.0080.0030.0040.0730.0000.0230.0000.0210.0220.0280.0360.066
src_bytes0.0010.0141.000-0.1680.114-0.0080.119-0.0020.028-0.0010.6660.7230.1300.7420.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.015
dst_bytes-0.0010.299-0.1681.0000.1930.0220.170-0.0040.0340.066-0.639-0.498-0.6120.0240.0090.0210.0000.0000.0000.0200.0950.2040.0000.0000.0030.0030.0000.0000.0040.0020.0000.0100.0000.0100.0000.0030.0030.0000.0000.057
hot0.0010.1090.1140.1931.0000.1130.8120.0030.0290.004-0.121-0.115-0.074-0.0180.0340.0090.0000.0000.0000.0910.0270.0000.0160.9730.0170.0170.0080.0080.0180.0150.0000.0550.0070.0420.0000.0170.0170.0080.0080.242
num_failed_logins0.0020.014-0.0080.0220.1131.0000.0050.0100.0150.006-0.018-0.018-0.028-0.0150.0100.1170.0000.0000.3330.0030.1350.2910.0000.0000.0040.0040.0370.0360.0060.0200.0000.0060.0000.0130.0530.0040.0040.0000.0000.102
num_compromised0.0000.0110.1190.1700.8120.0051.0000.0290.0310.007-0.097-0.091-0.0420.0030.0000.0000.0000.0000.0000.0080.2860.6450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0030.0000.0000.0000.0000.0000.002
num_root0.0000.013-0.002-0.0040.0030.0100.0291.0000.0480.015-0.055-0.054-0.079-0.0390.0000.0000.0000.0000.0000.0080.2860.6450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0030.0000.0000.0000.0000.0000.002
num_file_creations0.0000.0610.0280.0340.0290.0150.0310.0481.0000.031-0.036-0.035-0.050-0.0270.0040.0070.0000.0000.0000.0150.2060.0000.4340.0220.0000.0000.0010.0010.0000.0000.0000.0060.0000.0040.0000.0000.0000.0000.0000.123
num_access_files0.0020.019-0.0010.0660.0040.0060.0070.0150.0311.000-0.045-0.040-0.024-0.0240.0270.0080.0000.0000.0280.0730.2770.5800.0290.0000.0140.0140.0060.0070.0150.0000.0240.0360.0000.0350.0000.0140.0140.0060.0060.030
count0.001-0.2590.666-0.639-0.121-0.018-0.097-0.055-0.036-0.0451.0000.9510.5470.5870.7250.3180.0120.1300.0000.8020.0200.0050.0130.0720.8500.8510.3360.3240.9560.0660.2490.8610.0630.9600.0510.8520.8510.3410.3400.479
srv_count0.001-0.2500.723-0.498-0.115-0.018-0.091-0.054-0.035-0.0400.9511.0000.4430.7210.7200.2140.0060.2580.0000.4810.0110.0000.0070.0430.5350.5370.2790.2850.6250.0800.1490.7430.0740.9650.0310.5360.5360.2740.2720.295
dst_host_count-0.001-0.1610.130-0.612-0.074-0.028-0.042-0.079-0.050-0.0240.5470.4431.0000.0230.2930.0700.0310.0560.0060.6800.0260.0130.0250.0760.1660.1670.1290.1220.1870.0210.3010.1320.0130.3810.1070.1680.1680.1280.0620.361
dst_host_srv_count0.000-0.2170.7420.024-0.018-0.0150.003-0.039-0.027-0.0240.5870.7210.0231.0000.5410.3060.0110.1620.0000.3710.0100.0160.0390.1600.8080.8110.3220.3340.9370.1040.3710.9230.1110.7070.0570.8100.8110.3240.3580.234
protocol_type0.0000.1410.0000.0090.0340.0100.0000.0000.0040.0270.7250.7200.2930.5411.0000.4940.0080.1520.0010.5280.0130.0040.0090.0470.5830.5850.3040.3110.6610.0750.1210.7500.0790.9710.0080.5840.5850.2980.2960.444
flag0.0010.0800.0410.0210.0090.1170.0000.0000.0070.0080.3180.2140.0700.3060.4941.0000.0140.0190.0000.2300.0040.0000.0000.0210.9960.9990.9740.9960.9540.1570.0730.8000.3480.6430.0180.9970.9990.9570.9490.258
land0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0060.0310.0110.0080.0141.0000.0000.0000.0020.0000.0000.0000.0000.0130.0140.0000.0000.0010.0010.0410.0010.0000.0030.0960.0120.0030.0000.0000.000
wrong_fragment0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1300.2580.0560.1620.1520.0190.0001.0000.0000.0210.0000.0000.0000.0000.0230.0230.0120.0120.0120.0030.0060.0690.0150.0530.0000.0230.0230.0120.0120.018
urgent0.0000.0000.0000.0000.0000.3330.0000.0000.0000.0280.0000.0000.0060.0000.0010.0000.0000.0001.0000.0060.0950.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0270.0020.0000.0000.0000.0000.0000.060
logged_in0.0000.0110.0000.0200.0910.0030.0080.0080.0150.0730.8020.4810.6800.3710.5280.2300.0020.0210.0061.0000.0250.0120.0240.0890.1920.1930.0990.1010.2180.0030.1790.0750.0240.4760.0070.1920.1930.0980.0970.805
root_shell0.0010.0310.0000.0950.0270.1350.2860.2860.2060.2770.0200.0110.0260.0100.0130.0040.0000.0000.0950.0251.0000.3300.2460.0000.0040.0040.0020.0020.0040.0000.0000.0000.0000.0070.0030.0040.0040.0010.0010.487
su_attempted0.0000.0600.0000.2040.0000.2910.6450.6450.0000.5800.0050.0000.0130.0160.0040.0000.0000.0000.0000.0120.3301.0000.0420.0000.0010.0010.0000.0000.0020.0000.0040.0070.0000.0050.0000.0010.0010.0000.0000.010
num_shells0.0030.0000.0000.0000.0160.0000.0000.0000.4340.0290.0130.0070.0250.0390.0090.0000.0000.0000.0000.0240.2460.0421.0000.0000.0040.0040.0010.0010.0040.0000.0000.0140.0000.0110.0000.0040.0040.0010.0010.122
is_guest_login0.0000.0270.0000.0000.9730.0000.0000.0000.0220.0000.0720.0430.0760.1600.0470.0210.0000.0000.0000.0890.0000.0000.0001.0000.0170.0170.0080.0090.0180.0090.0010.0570.0000.0440.0000.0170.0170.0090.0080.359
serror_rate0.0000.0210.0000.0030.0170.0040.0000.0000.0000.0140.8500.5350.1660.8080.5830.9960.0130.0230.0000.1920.0040.0010.0040.0171.0000.9960.1110.1130.8480.0280.0580.7100.0230.5460.0100.9960.9950.1090.1080.234
srv_serror_rate0.0000.0220.0000.0030.0170.0040.0000.0000.0000.0140.8510.5370.1670.8110.5850.9990.0140.0230.0000.1930.0040.0010.0040.0170.9961.0000.1110.1140.8510.0150.0590.7130.0090.5480.0100.9970.9990.1090.1080.233
rerror_rate0.0040.0370.0000.0000.0080.0370.0000.0000.0010.0060.3360.2790.1290.3220.3040.9740.0000.0120.0000.0990.0020.0000.0010.0080.1110.1111.0000.9750.3100.0080.0490.2690.0670.2570.0160.1110.1110.9770.9250.087
srv_rerror_rate0.0040.0360.0000.0000.0080.0360.0000.0000.0010.0070.3240.2850.1220.3340.3110.9960.0000.0120.0000.1010.0020.0000.0010.0090.1130.1140.9751.0000.3250.1540.0450.2810.2180.2640.0150.1130.1140.9570.9510.200
same_srv_rate0.0000.0080.0000.0040.0180.0060.0000.0000.0000.0150.9560.6250.1870.9370.6610.9540.0010.0120.0000.2180.0040.0020.0040.0180.8480.8510.3100.3251.0000.1280.0640.8340.0740.6350.0140.8500.8510.3210.3510.239
diff_srv_rate0.0000.0030.0000.0020.0150.0200.0000.0000.0000.0000.0660.0800.0210.1040.0750.1570.0010.0030.0000.0030.0000.0000.0000.0090.0280.0150.0080.1540.1281.0000.0210.1070.6420.0720.0000.0280.0150.0130.1620.489
srv_diff_host_rate0.0040.0040.0000.0000.0000.0000.0000.0000.0000.0240.2490.1490.3010.3710.1210.0730.0410.0060.0000.1790.0000.0040.0000.0010.0580.0590.0490.0450.0640.0211.0000.0850.0040.1120.0020.0590.0590.0420.0150.272
dst_host_same_srv_rate0.0020.0730.0000.0100.0550.0060.0040.0040.0060.0360.8610.7430.1320.9230.7500.8000.0010.0690.0040.0750.0000.0070.0140.0570.7100.7130.2690.2810.8340.1070.0851.0000.0990.7390.0030.7120.7130.2700.3060.104
dst_host_diff_srv_rate0.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0630.0740.0130.1110.0790.3480.0000.0150.0270.0240.0000.0000.0000.0000.0230.0090.0670.2180.0740.6420.0040.0991.0000.0390.0830.0230.0090.0640.2280.681
dst_host_same_src_port_rate0.0000.0230.0000.0100.0420.0130.0030.0030.0040.0350.9600.9650.3810.7070.9710.6430.0030.0530.0020.4760.0070.0050.0110.0440.5460.5480.2570.2640.6350.0720.1120.7390.0391.0000.0100.5470.5480.2520.2550.535
dst_host_srv_diff_host_rate0.0020.0000.0000.0000.0000.0530.0000.0000.0000.0000.0510.0310.1070.0570.0080.0180.0960.0000.0000.0070.0030.0000.0000.0000.0100.0100.0160.0150.0140.0000.0020.0030.0830.0101.0000.0100.0110.0030.0160.265
dst_host_serror_rate0.0000.0210.0000.0030.0170.0040.0000.0000.0000.0140.8520.5360.1680.8100.5840.9970.0120.0230.0000.1920.0040.0010.0040.0170.9960.9970.1110.1130.8500.0280.0590.7120.0230.5470.0101.0000.9980.1090.1080.235
dst_host_srv_serror_rate0.0000.0220.0000.0030.0170.0040.0000.0000.0000.0140.8510.5360.1680.8110.5850.9990.0030.0230.0000.1930.0040.0010.0040.0170.9950.9990.1110.1140.8510.0150.0590.7130.0090.5480.0110.9981.0000.1090.1080.233
dst_host_rerror_rate0.0040.0280.0000.0000.0080.0000.0000.0000.0000.0060.3410.2740.1280.3240.2980.9570.0000.0120.0000.0980.0010.0000.0010.0090.1090.1090.9770.9570.3210.0130.0420.2700.0640.2520.0030.1090.1091.0000.9270.067
dst_host_srv_rerror_rate0.0040.0360.0000.0000.0080.0000.0000.0000.0000.0060.3400.2720.0620.3580.2960.9490.0000.0120.0000.0970.0010.0000.0010.0080.1080.1080.9250.9510.3510.1620.0150.3060.2280.2550.0160.1080.1080.9271.0000.214
label0.0000.0660.0150.0570.2420.1020.0020.0020.1230.0300.4790.2950.3610.2340.4440.2580.0000.0180.0600.8050.4870.0100.1220.3590.2340.2330.0870.2000.2390.4890.2720.1040.6810.5350.2650.2350.2330.0670.2141.000

Synthetic

Unnamed: 0durationsrc_bytesdst_byteshotnum_failed_loginsnum_compromisednum_rootnum_file_creationsnum_access_filescountsrv_countdst_host_countdst_host_srv_countprotocol_typeflaglandwrong_fragmenturgentlogged_inroot_shellsu_attemptednum_shellsis_guest_loginserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratelabel
Unnamed: 01.000-0.001-0.001-0.001-0.002-0.000-0.0010.000-0.0000.001-0.000-0.001-0.001-0.0020.0040.0000.0020.0030.0000.0040.0000.0000.0040.0000.0010.0010.0000.0000.0020.0040.0010.0000.0050.0040.0010.0010.0020.0000.0030.002
duration-0.0011.0000.1370.4720.0940.0110.0130.0170.0390.025-0.385-0.298-0.278-0.0550.1990.0350.0000.0000.0000.0240.0230.0420.0000.0310.0500.0500.0330.0320.0320.0060.0110.0750.0010.0100.0000.0500.0500.0260.0310.079
src_bytes-0.0010.1371.0000.2280.141-0.0010.1130.0470.0290.0220.3280.4870.0240.7410.0000.0330.0000.0000.0000.0050.0000.0000.0000.0110.0030.0030.0030.0030.0000.0000.0000.0060.0000.0030.0000.0030.0030.0000.0000.014
dst_bytes-0.0010.4720.2281.0000.1890.0210.1390.0150.0280.066-0.633-0.375-0.5610.1480.0090.0130.0580.0000.0000.0260.0000.0490.0000.0060.0110.0110.0030.0020.0120.0000.0000.0090.0040.0090.0200.0100.0110.0040.0030.059
hot-0.0020.0940.1410.1891.0000.0930.6480.0110.0510.002-0.129-0.117-0.0870.0010.0270.0140.0000.0000.0000.0960.0230.0000.0000.8520.0300.0310.0140.0140.0350.0110.0040.0410.0030.0330.0000.0310.0310.0140.0140.208
num_failed_logins-0.0000.011-0.0010.0210.0931.0000.0010.0020.007-0.001-0.020-0.020-0.022-0.0080.0080.1110.0000.0000.0000.0060.0000.0000.0000.0000.0090.0090.0380.0380.0110.0000.0010.0120.0000.0100.0040.0090.0090.0040.0030.161
num_compromised-0.0010.0130.1130.1390.6480.0011.0000.0380.0240.006-0.085-0.076-0.0550.0100.0020.0070.0000.0000.3540.0130.3000.5090.0000.0220.0020.0020.0000.0000.0040.0000.0000.0060.0000.0030.0000.0020.0020.0000.0000.004
num_root0.0000.0170.0470.0150.0110.0020.0381.0000.0250.007-0.073-0.068-0.069-0.0160.0000.0000.0000.0000.4080.0110.2450.5770.0000.0310.0000.0000.0000.0000.0030.0000.0000.0040.0000.0020.0000.0000.0000.0000.0000.003
num_file_creations-0.0000.0390.0290.0280.0510.0070.0240.0251.0000.017-0.029-0.027-0.027-0.0080.0030.0300.0000.0000.0000.0160.0000.0000.0000.0490.0030.0030.0000.0000.0050.0000.0000.0060.0000.0050.0000.0030.0030.0000.0000.040
num_access_files0.0010.0250.0220.0660.002-0.0010.0060.0070.0171.000-0.050-0.039-0.0280.0010.0210.0140.0000.0000.5000.0770.1600.5200.0000.0220.0240.0240.0120.0110.0290.0000.0310.0230.0000.0280.0000.0240.0240.0110.0110.032
count-0.000-0.3850.328-0.633-0.129-0.020-0.085-0.073-0.029-0.0501.0000.8040.6250.4180.7200.3290.0090.0930.0000.7840.0140.0030.0150.0730.8110.8120.2990.2920.9520.0800.2720.8440.0860.9470.0510.8140.8130.3060.3130.473
srv_count-0.001-0.2980.487-0.375-0.117-0.020-0.076-0.068-0.027-0.0390.8041.0000.4510.6410.7280.2190.0050.2480.0000.3540.0050.0000.0070.0330.4870.4890.2400.2440.5910.0550.1230.7150.0490.9520.0230.4880.4890.2360.2340.223
dst_host_count-0.001-0.2780.024-0.561-0.087-0.022-0.055-0.069-0.027-0.0280.6250.4511.0000.1210.2520.1500.0290.0690.0080.7540.0190.0120.0220.0680.3180.3200.1240.1220.3730.0260.3090.1980.0130.3150.1190.3210.3210.1280.1000.407
dst_host_srv_count-0.002-0.0550.7410.1480.001-0.0080.010-0.016-0.0080.0010.4180.6410.1211.0000.5460.3120.0070.1350.0000.4950.0100.0110.0420.1310.7800.7830.2950.3030.9380.0730.3830.9080.0770.7200.0520.7830.7840.3000.3270.303
protocol_type0.0040.1990.0000.0090.0270.0080.0020.0000.0030.0210.7200.7280.2520.5461.0000.5120.0050.1610.0000.3940.0070.0020.0080.0370.5450.5470.2680.2720.6380.0500.0890.7180.0520.9610.0210.5460.5470.2640.2610.365
flag0.0000.0350.0330.0130.0140.1110.0070.0000.0300.0140.3290.2190.1500.3120.5121.0000.0200.0360.0020.4940.0120.0100.0070.0360.9860.9900.9650.9780.9480.1090.1150.7770.1840.6540.0270.9890.9900.9540.9400.260
land0.0020.0000.0000.0580.0000.0000.0000.0000.0000.0000.0090.0050.0290.0070.0050.0201.0000.0000.0000.0030.0000.0000.0000.0000.0080.0070.0000.0000.0020.0000.0280.0050.0000.0050.1110.0070.0000.0000.0000.001
wrong_fragment0.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0930.2480.0690.1350.1610.0360.0001.0000.0000.0280.0000.0000.0000.0010.0400.0410.0200.0200.0300.0030.0090.0530.0140.0420.0090.0410.0410.0190.0190.020
urgent0.0000.0000.0000.0000.0000.0000.3540.4080.0000.5000.0000.0000.0080.0000.0000.0020.0000.0001.0000.0000.0000.3540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.002
logged_in0.0040.0240.0050.0260.0960.0060.0130.0110.0160.0770.7840.3540.7540.4950.3940.4940.0030.0280.0001.0000.0180.0090.0270.0930.3130.3140.1520.1540.3730.0040.2100.1990.0290.3460.0120.3140.3140.1500.1490.784
root_shell0.0000.0230.0000.0000.0230.0000.3000.2450.0000.1600.0140.0050.0190.0100.0070.0120.0000.0000.0000.0181.0000.1530.2270.0000.0050.0050.0020.0020.0060.0000.0000.0030.0000.0020.0030.0050.0050.0020.0020.354
su_attempted0.0000.0420.0000.0490.0000.0000.5090.5770.0000.5200.0030.0000.0120.0110.0020.0100.0000.0000.3540.0090.1531.0000.0000.0160.0020.0020.0000.0000.0030.0000.0060.0040.0000.0030.0000.0020.0020.0000.0000.005
num_shells0.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0070.0220.0420.0080.0070.0000.0000.0000.0270.2270.0001.0000.0020.0090.0090.0040.0040.0090.0000.0000.0140.0000.0100.0000.0090.0090.0040.0040.080
is_guest_login0.0000.0310.0110.0060.8520.0000.0220.0310.0490.0220.0730.0330.0680.1310.0370.0360.0000.0010.0000.0930.0000.0160.0021.0000.0290.0290.0140.0140.0340.0050.0040.0440.0000.0340.0000.0290.0290.0140.0140.306
serror_rate0.0010.0500.0030.0110.0300.0090.0020.0000.0030.0240.8110.4870.3180.7800.5450.9860.0080.0400.0000.3130.0050.0020.0090.0291.0000.9910.2040.2070.8160.0440.1070.6680.0380.5000.0190.9910.9910.2010.1980.390
srv_serror_rate0.0010.0500.0030.0110.0310.0090.0020.0000.0030.0240.8120.4890.3200.7830.5470.9900.0070.0410.0000.3140.0050.0020.0090.0290.9911.0000.2050.2080.8200.0320.1080.6710.0250.5020.0190.9930.9940.2020.1990.389
rerror_rate0.0000.0330.0030.0030.0140.0380.0000.0000.0000.0120.2990.2400.1240.2950.2680.9650.0000.0200.0000.1520.0020.0000.0040.0140.2040.2051.0000.9690.2830.0050.0160.2290.0560.2200.0120.2050.2050.9650.9290.089
srv_rerror_rate0.0000.0320.0030.0020.0140.0380.0000.0000.0000.0110.2920.2440.1220.3030.2720.9780.0000.0200.0000.1540.0020.0000.0040.0140.2070.2080.9691.0000.2910.1070.0130.2360.1640.2230.0120.2080.2080.9540.9450.154
same_srv_rate0.0020.0320.0000.0120.0350.0110.0040.0030.0050.0290.9520.5910.3730.9380.6380.9480.0020.0300.0000.3730.0060.0030.0090.0340.8160.8200.2830.2911.0000.0780.1260.8140.0440.6030.0240.8190.8200.2940.3200.429
diff_srv_rate0.0040.0060.0000.0000.0110.0000.0000.0000.0000.0000.0800.0550.0260.0730.0500.1090.0000.0030.0000.0040.0000.0000.0000.0050.0440.0320.0050.1070.0781.0000.0200.0750.5600.0450.0000.0440.0320.0060.1110.411
srv_diff_host_rate0.0010.0110.0000.0000.0040.0010.0000.0000.0000.0310.2720.1230.3090.3830.0890.1150.0280.0090.0000.2100.0000.0060.0000.0040.1070.1080.0160.0130.1260.0201.0000.0540.0060.0800.0110.1080.1080.0100.0120.294
dst_host_same_srv_rate0.0000.0750.0060.0090.0410.0120.0060.0040.0060.0230.8440.7150.1980.9080.7180.7770.0050.0530.0000.1990.0030.0040.0140.0440.6680.6710.2290.2360.8140.0750.0541.0000.0660.7120.0160.6700.6720.2340.2660.106
dst_host_diff_srv_rate0.0050.0010.0000.0040.0030.0000.0000.0000.0000.0000.0860.0490.0130.0770.0520.1840.0000.0140.0000.0290.0000.0000.0000.0000.0380.0250.0560.1640.0440.5600.0060.0661.0000.0160.0870.0380.0250.0460.1700.577
dst_host_same_src_port_rate0.0040.0100.0030.0090.0330.0100.0030.0020.0050.0280.9470.9520.3150.7200.9610.6540.0050.0420.0000.3460.0020.0030.0100.0340.5000.5020.2200.2230.6030.0450.0800.7120.0161.0000.0240.5010.5020.2170.2190.379
dst_host_srv_diff_host_rate0.0010.0000.0000.0200.0000.0040.0000.0000.0000.0000.0510.0230.1190.0520.0210.0270.1110.0090.0000.0120.0030.0000.0000.0000.0190.0190.0120.0120.0240.0000.0110.0160.0870.0241.0000.0190.0200.0070.0120.254
dst_host_serror_rate0.0010.0500.0030.0100.0310.0090.0020.0000.0030.0240.8140.4880.3210.7830.5460.9890.0070.0410.0000.3140.0050.0020.0090.0290.9910.9930.2050.2080.8190.0440.1080.6700.0380.5010.0191.0000.9940.2010.1990.391
dst_host_srv_serror_rate0.0020.0500.0030.0110.0310.0090.0020.0000.0030.0240.8130.4890.3210.7840.5470.9900.0000.0410.0000.3140.0050.0020.0090.0290.9910.9940.2050.2080.8200.0320.1080.6720.0250.5020.0200.9941.0000.2010.1990.390
dst_host_rerror_rate0.0000.0260.0000.0040.0140.0040.0000.0000.0000.0110.3060.2360.1280.3000.2640.9540.0000.0190.0000.1500.0020.0000.0040.0140.2010.2020.9650.9540.2940.0060.0100.2340.0460.2170.0070.2010.2011.0000.9290.088
dst_host_srv_rerror_rate0.0030.0310.0000.0030.0140.0030.0000.0000.0000.0110.3130.2340.1000.3270.2610.9400.0000.0190.0000.1490.0020.0000.0040.0140.1980.1990.9290.9450.3200.1110.0120.2660.1700.2190.0120.1990.1990.9291.0000.181
label0.0020.0790.0140.0590.2080.1610.0040.0030.0400.0320.4730.2230.4070.3030.3650.2600.0010.0200.0020.7840.3540.0050.0800.3060.3900.3890.0890.1540.4290.4110.2940.1060.5770.3790.2540.3910.3900.0880.1811.000

Missing values

Real

2023-12-26T00:01:12.216869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.

Synthetic

2023-12-26T00:03:23.268780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.

Real

2023-12-26T00:01:14.955708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Synthetic

2023-12-26T00:03:26.574327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Real

Unnamed: 0durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratelabel
001tcpsmtpSF10223890000010000000001100001008317500000000normal
110tcphttpSF37628500000100000000022000010025525510000000normal
220icmpecr_iSF10320000000000000000511511000010025525510100000dos
330icmpecr_iSF10320000000000000000511511000010025525510100000dos
440tcpprivateS00000000000000000025211100000255100001100dos
550icmpecr_iSF5200000000000000000511511000010025525510100000dos
660tcpprivateS000000000000000000245121100000255900001100dos
770tcpprivateREJ000000000000000002351800110002551800000011dos
880icmpecr_iSF10320000000000000000511511000010025525510100000dos
990tcphttpSF3041854000001000000000111200001006825510000000normal

Synthetic

Unnamed: 0durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratelabel
000tcpnameS000000000000000000102811000002551500001100dos
1113udpprivateSF10514600000000000000021000010025421600000000normal
220icmpecr_iSF11430000000000000000494492000010025525210100000dos
330icmpecr_iSF9480000000000000000500511000010025425110100000dos
440icmpecr_iSF7940000000000000000510496000010025325410100000dos
550icmpecr_iSF10320000000000000000509510000010025525510100000dos
660tcpremote_jobS000000000000000000146181100000255300001100dos
770icmpecr_iSF10320000000000000000511511000010025525510100000dos
880icmpecr_iSF9700000000000000000498498000010025525510100000dos
990icmpecr_iSF5510000000000000000484504000010025425410100000dos

Real

Unnamed: 0durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratelabel
4940114940110tcpefsS0000000000000000001431411000002551400001100dos
4940124940120icmpecr_iSF10320000000000000000511511000010025525510100000dos
4940134940130tcpprivateS000000000000000000205211000002551300001100dos
4940144940140tcpprinterS0000000000000000001271011000002551200001100dos
4940154940150icmpecr_iSF10320000000000000000511510000010025525510100000dos
4940164940160icmpecr_iSF5200000000000000000511511000010025525510100000dos
4940174940170tcpprivateS0000000000000000002651011000002551000001100dos
4940184940180icmpecr_iSF5200000000000000000511511000010025525510100000dos
4940194940190icmpecr_iSF10320000000000000000511511000010025525510100000dos
4940204940200tcpprivateREJ0000000000000000025570011000255700000011dos

Synthetic

Unnamed: 0durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratelabel
4940114940110icmpecr_iSF11280000000000000000476495000010025525510100000dos
4940124940120tcpshellOTH0000000000000000018110011000255100000011dos
4940134940130tcphttpSF22018360000010000000001014000010017325310000000normal
4940144940140icmpecr_iSF12350000000000000000507491000010025525510100000dos
4940154940150tcpprivateS00000000000000000054211000002551000001100dos
4940164940160icmpecr_iSF7030000000000000000495492000010025425510100000dos
4940174940173tcpftpS31961920000010000000005469000010019121510000000normal
4940184940180tcpnnspS0000000000000000002931911000002552500001100dos
4940194940190tcphttpSF22513461000001000000000530000100456010000000normal
4940204940200tcpnnspS0000000000000000002291111000002551400001100dos

Duplicate rows

Real

Unnamed: 0durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratelabel# duplicates
Dataset does not contain duplicate rows.

Synthetic

Unnamed: 0durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_ratelabel# duplicates
Dataset does not contain duplicate rows.